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50 pages, 1631 KiB  
Review
A Survey on Solving the Matrix Equation AXB = C with Applications
by Qing-Wen Wang, Lv-Ming Xie and Zi-Han Gao
Mathematics 2025, 13(3), 450; https://doi.org/10.3390/math13030450 - 28 Jan 2025
Viewed by 574
Abstract
This survey provides a comprehensive overview of the solutions to the matrix equation AXB=C over real numbers, complex numbers, quaternions, dual quaternions, dual split quaternions, and dual generalized commutative quaternions, including various special solutions. Additionally, we summarize the numerical [...] Read more.
This survey provides a comprehensive overview of the solutions to the matrix equation AXB=C over real numbers, complex numbers, quaternions, dual quaternions, dual split quaternions, and dual generalized commutative quaternions, including various special solutions. Additionally, we summarize the numerical algorithms for these special solutions. This matrix equation plays an important role in solving linear systems and control theory. We specifically explore the application of this matrix equation in color image processing, highlighting its unique value in this field. Taking the dual quaternion matrix equation AXB=C as an example, we design a scheme for simultaneously encrypting and decrypting two color images. The experimental results demonstrate that this scheme is highly feasible. Full article
(This article belongs to the Section A: Algebra and Logic)
Show Figures

Figure 1

Figure 1
<p>Scheme.</p>
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<p>Original image.</p>
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<p>Encrypted image.</p>
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<p>Decrypted image.</p>
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18 pages, 949 KiB  
Article
Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
by Chuang Gan, Weichun Wang, Yuchong Hu, Xin Zhao, Shi Dun, Qixiang Xiao, Wei Wang and Huadong Huang
Appl. Sci. 2025, 15(3), 1245; https://doi.org/10.3390/app15031245 - 26 Jan 2025
Viewed by 385
Abstract
Outsourcing storage to the cloud can save storage costs and is commonly used in businesses. It should fulfill two major goals: storage efficiency and data confidentiality. Encrypted deduplication can achieve both goals via performing deduplication to eliminate the duplicate data within encrypted data. [...] Read more.
Outsourcing storage to the cloud can save storage costs and is commonly used in businesses. It should fulfill two major goals: storage efficiency and data confidentiality. Encrypted deduplication can achieve both goals via performing deduplication to eliminate the duplicate data within encrypted data. Traditional encrypted deduplication generates the encryption key on the client side, which poses a risk of offline brute-force cracking of the outsourced data. Server-aided encryption schemes have been proposed to strengthen the confidentiality of encrypted deduplication by distributing the encryption process to dedicated servers. Existing schemes rely on expensive cryptographic primitives to provide a decentralized setting on the dedicated servers for scalability. However, this incurs substantial performance slowdown and can not be applied in practical encrypted deduplication storage systems. In this paper, we propose a new decentralized server-aided encrypted deduplication approach for outsourced storage, called ECDedup, which leverages secret sharing to achieve secure and efficient key management. We are the first to use the coding matrix as the encryption key to couple the encryption and encoding processes in encrypted deduplication. We also propose a acceleration scheme to speed up the encryption process of our ECDedup. We prototype ECDedup in cloud environments, and our experimental results based on the real-world backup datasets show that ECDedup can improve the client throughput by up to 51.9% compared to the state-of-the-art encrypted deduplication schemes. Full article
(This article belongs to the Special Issue Application of Deep Learning and Big Data Processing)
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Figure 1
<p>System architecture of ECDedup.</p>
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<p>Sharing the coding matrix among the key servers.</p>
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<p>An example of the fixed-size sampling.</p>
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<p>Two ways of generating matrix in ECDedup.</p>
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<p>Pipelining matrix generation and server-aided encryption.</p>
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<p>Sliding-window-based matrix generation.</p>
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<p>Comparison of different encryption schemes.</p>
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<p>Exp 3: Impact of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>n</mi> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> </semantics></math> for ECDedup.</p>
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<p>Exp 4: Comparison of client throughput for different encryption scheme.</p>
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<p>Exp 5: Average indexing time per chunk.</p>
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<p>Exp 6: Comparison of upload and download performance of different encryption schemes.</p>
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22 pages, 26819 KiB  
Article
A New Chaotic Color Image Encryption Algorithm Based on Memristor Model and Random Hybrid Transforms
by Yexia Yao, Xuemei Xu and Zhaohui Jiang
Appl. Sci. 2025, 15(2), 913; https://doi.org/10.3390/app15020913 - 17 Jan 2025
Viewed by 512
Abstract
This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system’s exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the [...] Read more.
This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system’s exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the chaotic system is proposed. To enhance encryption security, the pixel values of the image are preprocessed, and a hash function is used to generate a hash value, which is then incorporated into the secret keys generation process. Existing algorithms typically encrypt the three channels of a color image separately or perform encryption only at the pixel level, resulting in certain limitations in encryption effectiveness. To address this, this paper proposes a novel encryption algorithm based on 2D hyperchaotic maps that extends from single-channel encryption to multi-channel encryption (SEME-TDHM). The SEME-TDHM algorithm combines single-channel and multi-channel random scrambling, followed by local cross-diffusion of pixel values across different planes. By integrating both pixel-level and bit-level diffusion, the randomness of the image information distribution is significantly increased. Finally, the diffusion matrix is decomposed and restored to generate the encrypted color image. Simulation results and comparative analyses demonstrate that the SEME-TDHM algorithm outperforms existing algorithms in terms of encryption effectiveness. The encrypted image maintains a stable information entropy around 7.999, with average NPCR and UACI values close to the ideal benchmarks of 99.6169% and 33.4623%, respectively, further affirming its outstanding encryption effectiveness. Additionally, the histogram of the encrypted image shows a uniform distribution, and the correlation coefficient is nearly zero. These findings indicate that the SEME-TDHM algorithm successfully encrypts color images, providing strong security and practical utility. Full article
(This article belongs to the Special Issue Signal and Image Processing: From Theory to Applications)
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Figure 1

Figure 1
<p>System framework diagram.</p>
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<p>A new system.</p>
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<p>Phase diagrams for different maps.</p>
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<p>Bifurcation diagram.</p>
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<p>Lyapunov exponents spectrum.</p>
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<p>Permutation entropy and its comparison chart.</p>
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<p>0–1 test result.</p>
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<p>Diagram of the encryption process.</p>
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<p>Global scrambling.</p>
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<p>Encrypted and decrypted images, original images are (<b>a1</b>) Candy, 256 × 256, (<b>a2</b>) Baboon, 512 × 512, (<b>a3</b>) House, 614 × 409, (<b>a4</b>) Bird, 768 × 512, (<b>a5</b>) Pepper, 1000 × 1000, (<b>a6</b>) Aircraft, 1024 × 1024; its corresponding encrypted images are (<b>b1</b>–<b>b6</b>); its corresponding decrypted images are (<b>c1</b>–<b>c6</b>).</p>
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<p>Decryption with different keys.</p>
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<p>Histogram analysis of images and cipher images: Red represents the R channel; green represents the G channel; blue represents the B channel.</p>
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<p>Histogram analysis of images and cipher images: Red represents the R channel; green represents the G channel; blue represents the B channel.</p>
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<p>Three-channel adjacent pixel correlation of images and cipher images: red represents the distribution charts in horizontal direction; green represents the distribution charts in diagonal direction; blue represents the distribution charts in vertical direction.</p>
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<p>Noise test results of Candy, House and Pepper, Gaussian noise of (<b>a1</b>–<b>a3</b>) 1 × 10<sup>−7</sup>, (<b>b1</b>–<b>b3</b>) 1 × 10<sup>−6</sup>, Pepper and Salt noise of (<b>c1</b>–<b>c3</b>) 0.001, (<b>d1</b>–<b>d3</b>) 0.01.</p>
Full article ">Figure 14 Cont.
<p>Noise test results of Candy, House and Pepper, Gaussian noise of (<b>a1</b>–<b>a3</b>) 1 × 10<sup>−7</sup>, (<b>b1</b>–<b>b3</b>) 1 × 10<sup>−6</sup>, Pepper and Salt noise of (<b>c1</b>–<b>c3</b>) 0.001, (<b>d1</b>–<b>d3</b>) 0.01.</p>
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<p>Data loss analysis.</p>
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38 pages, 16379 KiB  
Article
Hyperbolic Sine Function Control-Based Finite-Time Bipartite Synchronization of Fractional-Order Spatiotemporal Networks and Its Application in Image Encryption
by Lvming Liu, Haijun Jiang, Cheng Hu, Haizheng Yu, Siyu Chen, Yue Ren, Shenglong Chen and Tingting Shi
Fractal Fract. 2025, 9(1), 36; https://doi.org/10.3390/fractalfract9010036 - 13 Jan 2025
Viewed by 532
Abstract
This work is devoted to the hyperbolic sine function (HSF) control-based finite-time bipartite synchronization of fractional-order spatiotemporal networks and its application in image encryption. Initially, the addressed networks adequately take into account the nature of anisotropic diffusion, i.e., the diffusion matrix can be [...] Read more.
This work is devoted to the hyperbolic sine function (HSF) control-based finite-time bipartite synchronization of fractional-order spatiotemporal networks and its application in image encryption. Initially, the addressed networks adequately take into account the nature of anisotropic diffusion, i.e., the diffusion matrix can be not only non-diagonal but also non-square, without the conservative requirements in plenty of the existing literature. Next, an equation transformation and an inequality estimate for the anisotropic diffusion term are established, which are fundamental for analyzing the diffusion phenomenon in network dynamics. Subsequently, three control laws are devised to offer a detailed discussion for HSF control law’s outstanding performances, including the swifter convergence rate, the tighter bound of the settling time and the suppression of chattering. Following this, by a designed chaotic system with multi-scroll chaotic attractors tested with bifurcation diagrams, Poincaré map, and Turing pattern, several simulations are pvorided to attest the correctness of our developed findings. Finally, a formulated image encryption algorithm, which is evaluated through imperative security tests, reveals the effectiveness and superiority of the obtained results. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) The strange attractor of system (40) at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1.33</mn> </mrow> </semantics></math>. (<b>b</b>) The strange attractor of system (40) at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>0.66</mn> </mrow> </semantics></math>. (<b>c</b>) The strange attractor of system (40) at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) The spatiotemporal evolution of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) The spatiotemporal evolution of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) The spatiotemporal evolution of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) The Turing pattern of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) The Turing pattern of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) The Turing pattern of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Bifurcation diagram. (<b>b</b>) Poincaré map: the projection on the plane is <math display="inline"><semantics> <mrow> <mn>5</mn> <mi>x</mi> <mo>−</mo> <mn>6.1</mn> <mi>y</mi> <mo>+</mo> <mn>0.92</mn> <mi>z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Network topology. (<b>b</b>) The time evolutions of error <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> without control.</p>
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<p>(<b>a</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map. (<b>b</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map. (<b>c</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map.</p>
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<p>(<b>a</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> under the control protocol (20).</p>
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<p>(<b>a</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map under control protocol (20). (<b>b</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map undercontrol protocol (20). (<b>c</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map undercontrol protocol (20).</p>
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<p>(<b>a</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> under the control protocol (30).</p>
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<p>(<b>a</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map under control protocol (30). (<b>b</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map under control protocol (30). (<b>c</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, with contour map under control protocol (30).</p>
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<p>(<b>a</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 14
<p>The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> under the control law (35).</p>
Full article ">Figure 15
<p>(<b>a</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map under control law (35). (<b>b</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map under control law (35). (<b>c</b>) The spatiotemporal evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with contour map under control law (35).</p>
Full article ">Figure 16
<p>(<b>a</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) The time evolutions of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) The control inputs of the control law (35) in this work and [<a href="#B7-fractalfract-09-00036" class="html-bibr">7</a>]. (<b>b</b>) The synchronization errors in this work and [<a href="#B7-fractalfract-09-00036" class="html-bibr">7</a>]. (<math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mrow> <mn>0</mn> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mrow> <mn>0</mn> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> are the controllor and error in [<a href="#B7-fractalfract-09-00036" class="html-bibr">7</a>], respectively).</p>
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<p>(<b>a</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mn>2</mn> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>t</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mn>6</mn> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>t</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>The image encryption and decryption algorithm.</p>
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<p>(<b>a</b>) The plaintext image to be encrypted. (<b>b</b>) The content obtained via scanning the plaintext image. (<b>c</b>) The ciphertext image. (<b>d</b>) The decryption image.</p>
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<p>(<b>a</b>) The histogram of the plaintext image. (<b>b</b>) The histogram of the ciphertext image.</p>
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<p>(<b>a1</b>,<b>b1</b>,<b>c1</b>,<b>d1</b>): The scatter plots of adjacent horizontal, vertical, positive diagonal and counter-diagonal for plaintext image, respectively. (<b>a2</b>,<b>b2</b>,<b>c2</b>,<b>d2</b>): the scatter plots of adjacent horizontal, vertical, positive diagonal and counter diagonal for ciphertext image, respectively.</p>
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<p>(<b>a1</b>,<b>b1</b>,<b>c1</b>,<b>d1</b>): The scatter plots of adjacent horizontal, vertical, positive diagonal and counter-diagonal for plaintext image, respectively. (<b>a2</b>,<b>b2</b>,<b>c2</b>,<b>d2</b>): the scatter plots of adjacent horizontal, vertical, positive diagonal and counter diagonal for ciphertext image, respectively.</p>
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<p>The decrypted image with <math display="inline"><semantics> <mrow> <msup> <mi>s</mi> <mn>0</mn> </msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>14</mn> </mrow> </msup> <mo>,</mo> <mspace width="3.33333pt"/> <mn>0</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mn>0</mn> <mo>)</mo> </mrow> <mi>T</mi> </msup> </mrow> </semantics></math>.</p>
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<p>(<b>a1</b>) The ciphertext image with <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> salt–pepper noise. (<b>a2</b>). The decryption image for the ciphertext image with <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> salt–pepper noise. (<b>b1</b>) The ciphertext image with <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> salt–pepper noise and <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> Gaussian noise. (<b>b2</b>) The decryption image for the ciphertext image with <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> salt–pepper noise and <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> Gaussian noise. (<b>c1</b>) The ciphertext image with <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> salt–pepper noise, <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> Gaussian noise, and <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>%</mo> </mrow> </semantics></math> speckle noise. (<b>c2</b>) The decryption image for the ciphertext image with <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> salt–pepper noise, <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> Gaussian noise, and <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>%</mo> </mrow> </semantics></math> speckle noise.</p>
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<p>(<b>a1</b>) The ciphertext image with a center shear attack at 25% intensity. (<b>a2</b>) The diffused ciphertext image with a center shear attack at 25% intensity. (<b>a3</b>) The decryption image for the ciphertext image with a center shear attack at 25% intensity. (<b>b1</b>) The ciphertext image with a perimeter shear attack at 50% intensity. (<b>b2</b>) The diffused ciphertext image with a perimeter shear attack at 50% intensity. (<b>b3</b>) The decryption image for the ciphertext image with a perimeter shear attack at 50% intensity.</p>
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22 pages, 18396 KiB  
Article
A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption
by Yuzhou Xi, Yu Ning, Jie Jin and Fei Yu
Mathematics 2024, 12(24), 3948; https://doi.org/10.3390/math12243948 - 15 Dec 2024
Viewed by 818
Abstract
Cryptography is one of the most important branches of information security. Cryptography ensures secure communication and data privacy, and it has been increasingly applied in healthcare and related areas. As a significant cryptographic method, the Hill cipher has attracted significant attention from experts [...] Read more.
Cryptography is one of the most important branches of information security. Cryptography ensures secure communication and data privacy, and it has been increasingly applied in healthcare and related areas. As a significant cryptographic method, the Hill cipher has attracted significant attention from experts and scholars. To enhance the security of the traditional Hill cipher (THC) and expand its application in medical image encryption, a novel dynamic Hill cipher with Arnold scrambling technique (DHCAST) is proposed in this work. Unlike the THC, the proposed DHCAST uses a time-varying matrix as its secret key, which greatly increases the security of the THC, and the new DHCAST is successfully applied in medical images encryption. In addition, the new DHCAST method employs the Zeroing Neural Network (ZNN) in its decryption to find the time-varying inversion key matrix (TVIKM). In order to enhance the efficiency of the ZNN for solving the TVIKM, a new fuzzy zeroing neural network (NFZNN) model is constructed, and the convergence and robustness of the NFZNN model are validated by both theoretical analysis and experiment results. Simulation experiments show that the convergence time of the NFZNN model is about 0.05 s, while the convergence time of the traditional Zeroing Neural Network (TZNN) model is about 2 s, which means that the convergence speed of the NFZNN model is about 400 times that of the TZNN model. Moreover, the Peak Signal to Noise Ratio (PSNR) and Number of Pixel Change Rate (NPCR) of the proposed DHCAST algorithm reach 9.51 and 99.74%, respectively, which effectively validates its excellent encryption quality and attack prevention ability. Full article
(This article belongs to the Special Issue Chaotic Systems and Their Applications, 2nd Edition)
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<p>The THC and proposed DHCAST. (<b>a</b>) The THC. (<b>b</b>) The proposed DHCAST.</p>
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<p>The structure of FLS.</p>
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<p>State solutions and residual errors of the NFZNN and other models for solving the TVIKM <math display="inline"><semantics> <mrow> <mi>A</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>D</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> without noise. (<b>a</b>) State solutions of the models without noise. (<b>b</b>) Residual errors of the models without noise.</p>
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<p>State solutions and residual errors of the NFZNN model and other models for solving the TVIKM matrix <math display="inline"><semantics> <mrow> <mi>A</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>D</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with combined noises. (<b>a</b>) State solutions of the models with combined noises. (<b>b</b>) Residual errors of the models with combined noises.</p>
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<p>The DHCAST encryption and decryption simulation results for an ultrasound cardiology color image <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>528</mn> <mo>×</mo> <mn>528</mn> <mo>×</mo> <mn>3</mn> <mo>)</mo> </mrow> </semantics></math>. (<b>a</b>) Key time <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>5.7</mn> </mrow> </semantics></math> s. (<b>b</b>) Key time <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>14.3</mn> </mrow> </semantics></math> s.</p>
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<p>The DHCAST encryption and decryption simulation results of MRI grayscale image <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>528</mn> <mo>×</mo> <mn>528</mn> <mo>×</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>. (<b>a</b>) Key time <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>5.7</mn> </mrow> </semantics></math> s. (<b>b</b>) Key time <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>14.3</mn> </mrow> </semantics></math> s.</p>
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<p>The DHCAST encryption and decryption simulation results of MRI grayscale image <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>528</mn> <mo>×</mo> <mn>528</mn> <mo>×</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>. (<b>a</b>) Key time <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>5.7</mn> </mrow> </semantics></math> s. (<b>b</b>) Key time <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>14.3</mn> </mrow> </semantics></math> s.</p>
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15 pages, 263 KiB  
Article
A Matrix Multiplication Approach to Quantum-Safe Cryptographic Systems
by Luis Adrián Lizama-Pérez
Cryptography 2024, 8(4), 56; https://doi.org/10.3390/cryptography8040056 - 15 Dec 2024
Viewed by 914
Abstract
This paper introduces a novel approach based on matrix multiplication in Fpn×n, which enables methods for public key exchange, user authentication, digital signatures, blockchain integration, and homomorphic encryption. Unlike traditional algorithms that rely on integer factorization or discrete [...] Read more.
This paper introduces a novel approach based on matrix multiplication in Fpn×n, which enables methods for public key exchange, user authentication, digital signatures, blockchain integration, and homomorphic encryption. Unlike traditional algorithms that rely on integer factorization or discrete logarithms, our approach utilizes matrix factorization, rendering it resistant to current quantum cryptanalysis techniques. This method enhances confidentiality by ensuring secure communication and facilitating user authentication through public key validation. We have incorporated a method that allows a Certification Authority to certify the public keys. Furthermore, the incorporation of digital signatures ensures nonrepudiation, while the system functions as a blockchain technology to enhance transaction security. A key innovation of this approach is its capability to perform homomorphic encryption. Our approach has practical applications in artificial intelligence, robotics, and image processing. Full article
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<p><math display="inline"><semantics> <msub> <mi>P</mi> <mi>b</mi> </msub> </semantics></math> is Bob’s public key, defined as <math display="inline"><semantics> <mrow> <msup> <mi>g</mi> <msub> <mi>x</mi> <mi>b</mi> </msub> </msup> <mspace width="3.33333pt"/> <mo form="prefix">mod</mo> <mspace width="0.277778em"/> <mi>p</mi> </mrow> </semantics></math>. By using <math display="inline"><semantics> <msub> <mi>y</mi> <mi>a</mi> </msub> </semantics></math> as a different random value for each message, Alice sends an encrypted message by multiplying <span class="html-italic">m</span> by <math display="inline"><semantics> <msub> <mi>k</mi> <mi>s</mi> </msub> </semantics></math>. Bob retrieves <span class="html-italic">m</span> by applying the inverse of <math display="inline"><semantics> <msub> <mi>k</mi> <mi>s</mi> </msub> </semantics></math>, denoted as <math display="inline"><semantics> <msup> <mrow> <msub> <mi>k</mi> <mi>s</mi> </msub> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p>
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<p>Alice derives the shared key as <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">k</mi> <mi mathvariant="bold">ab</mi> </msub> <mo>=</mo> <msup> <mrow> <msub> <mi mathvariant="bold">K</mi> <mi mathvariant="bold">u</mi> </msub> </mrow> <mi>x</mi> </msup> <mo>=</mo> <msup> <mrow> <mo>{</mo> <mi mathvariant="bold">zw</mi> <mo>}</mo> </mrow> <mi>x</mi> </msup> </mrow> </semantics></math>.</p>
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<p>Alice derives the shared key as <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">k</mi> <mi mathvariant="bold">ab</mi> </msub> <mo>=</mo> <msup> <mrow> <msub> <mi mathvariant="bold">K</mi> <msub> <mi mathvariant="bold">u</mi> <mi mathvariant="bold">b</mi> </msub> </msub> </mrow> <msub> <mi>x</mi> <mi>a</mi> </msub> </msup> <mo>=</mo> <msup> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">z</mi> <mi mathvariant="bold">b</mi> </msub> <msub> <mi mathvariant="bold">w</mi> <mi mathvariant="bold">b</mi> </msub> <mo>}</mo> </mrow> <mrow> <msub> <mi>x</mi> <mi>a</mi> </msub> <msub> <mi>y</mi> <mi>b</mi> </msub> </mrow> </msup> </mrow> </semantics></math>. Bob obtains the secret key <math display="inline"><semantics> <msub> <mi mathvariant="bold">k</mi> <mi mathvariant="bold">ba</mi> </msub> </semantics></math> by computing <math display="inline"><semantics> <mrow> <msup> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">z</mi> <mi mathvariant="bold">b</mi> </msub> <msub> <mi mathvariant="bold">w</mi> <mi mathvariant="bold">b</mi> </msub> <mo>}</mo> </mrow> <mrow> <msub> <mi>x</mi> <mi>a</mi> </msub> <msub> <mi>y</mi> <mi>b</mi> </msub> </mrow> </msup> <mo>=</mo> <msup> <mrow> <msub> <mi mathvariant="bold">w</mi> <mi mathvariant="bold">b</mi> </msub> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>·</mo> <msup> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">w</mi> <mi mathvariant="bold">b</mi> </msub> <msub> <mi mathvariant="bold">z</mi> <mi mathvariant="bold">b</mi> </msub> <mo>}</mo> </mrow> <mrow> <msub> <mi>x</mi> <mi>a</mi> </msub> <msub> <mi>y</mi> <mi>b</mi> </msub> </mrow> </msup> <mo>·</mo> <msub> <mi mathvariant="bold">w</mi> <mi mathvariant="bold">b</mi> </msub> </mrow> </semantics></math>.</p>
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<p>In a Man-in-the-Middle (MitM) scenario, Eve and Bob share <math display="inline"><semantics> <msub> <mi mathvariant="bold">k</mi> <mi mathvariant="bold">eb</mi> </msub> </semantics></math>. However, Eve is unable to establish a key with Alice, as she derives the secret key by first accessing Bob’s public key from the service and then raising it to <math display="inline"><semantics> <msub> <mi>x</mi> <mi>a</mi> </msub> </semantics></math>.</p>
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<p>User Authentication.</p>
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<p>Digital signature scheme. Bob verifies that <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">d</mi> <mn mathvariant="bold">1</mn> </msub> <mo>·</mo> <msub> <mi mathvariant="bold">d</mi> <mn mathvariant="bold">2</mn> </msub> <mo>=</mo> <msub> <mi mathvariant="bold">d</mi> <mn mathvariant="bold">2</mn> </msub> <mo>·</mo> <msub> <mi mathvariant="bold">d</mi> <mn mathvariant="bold">1</mn> </msub> <mo>=</mo> <msup> <mrow> <msub> <mi mathvariant="bold">K</mi> <mi mathvariant="bold">u</mi> </msub> </mrow> <mi>h</mi> </msup> </mrow> </semantics></math>, where <span class="html-italic">h</span> represents the hash code of the message to be signed.</p>
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<p>The public key is <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">K</mi> <msub> <mi mathvariant="bold">u</mi> <mn mathvariant="bold">0</mn> </msub> </msub> <mo>=</mo> <msup> <mrow> <mo>{</mo> <mi mathvariant="bold">zw</mi> <mo>}</mo> </mrow> <mrow> <mn>2</mn> <mi>y</mi> </mrow> </msup> </mrow> </semantics></math>, and the verification rule is <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">K</mi> <msub> <mi mathvariant="bold">u</mi> <mi mathvariant="bold">i</mi> </msub> </msub> <mo>=</mo> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">K</mi> <msub> <mi mathvariant="bold">u</mi> <mrow> <mi mathvariant="bold">i</mi> <mo>−</mo> <mn mathvariant="bold">1</mn> </mrow> </msub> </msub> </mrow> <msup> <mrow> <mo>}</mo> </mrow> <msub> <mi>h</mi> <mi>i</mi> </msub> </msup> </mrow> </semantics></math>.</p>
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26 pages, 9163 KiB  
Article
A Novel Multi-Channel Image Encryption Algorithm Leveraging Pixel Reorganization and Hyperchaotic Maps
by Wei Feng, Jiaxin Yang, Xiangyu Zhao, Zhentao Qin, Jing Zhang, Zhengguo Zhu, Heping Wen and Kun Qian
Mathematics 2024, 12(24), 3917; https://doi.org/10.3390/math12243917 - 12 Dec 2024
Cited by 7 | Viewed by 1074
Abstract
Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to [...] Read more.
Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to jointly generate chaotic sequences, ensuring a larger key space and better randomness. During the encryption process, we first convert input images into two fused matrices through pixel reorganization. Then, we apply two rounds of scrambling and diffusion operations, coupled with one round of substitution operations, to the high 4-bit matrix. For the low 4-bit matrix, we conduct one round of substitution and diffusion operations. Extensive experiments and comparisons demonstrate that MIEA-PRHM outperforms many recent encryption algorithms in various aspects, especially in encryption efficiency. Full article
(This article belongs to the Special Issue Chaos-Based Secure Communication and Cryptography, 2nd Edition)
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<p>State value distributions of 2D-SCPM and 2D-ECHM: the first two 3D bifurcation diagrams illustrate the distributions of <math display="inline"><semantics> <msub> <mi>x</mi> <mi>i</mi> </msub> </semantics></math> for two hyperchaotic maps, whereas the subsequent two 3D bifurcation diagrams depict the distributions of <math display="inline"><semantics> <msub> <mi>y</mi> <mi>i</mi> </msub> </semantics></math>.</p>
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<p>Encryption process of our proposed MIEA-PRHM.</p>
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<p>Simple example of row–column joint scrambling: The blue squares represent the pixels of the input pixel matrix, the red squares denote the pixels currently being scrambled, and the gray squares indicate the scrambled pixels.</p>
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<p>Simple example of vector-level dynamic rotational diffusion: The blue squares represent the pixels of the input pixel matrix, the red squares denote the pixel regions currently undergoing diffusion, and the gray squares signify the diffused pixels. The bold gray arrows indicate the diffusion direction at each stage.</p>
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<p>Simple example of dual-operation dynamic partition substitution: The pink squares represent the pixels of the input pixel matrix, the red squares denote the pixels currently undergoing substitution, and the gray squares signify the substituted pixels.</p>
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<p>Decryption process of our proposed MIEA-PRHM.</p>
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<p>Visual assessment results for MIEA-PRHM: in the <b>first row</b>, six original test images, denoted as 5.1.09, 5.2.09, boat.512, 4.1.07, avion, and beeflowr, are concurrently input; the <b>second row</b> presents the corresponding encrypted images obtained simultaneously; and the <b>final row</b> exhibits the respective decrypted images.</p>
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<p>Key sensitivity experimental results for MIEA-PRHM: (<b>a1</b>) test image 5.2.08; (<b>a2</b>) ciphertext of 5.2.08 obtained with <math display="inline"><semantics> <mover accent="true"> <mi mathvariant="bold">K</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>; (<b>b1</b>) ciphertext obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b2</b>) obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b3</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>α</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>α</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b4</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b5</b>) obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b6</b>) obtained with <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mover accent="true"> <mi>y</mi> <mo stretchy="false">^</mo> </mover> <mn>0</mn> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b7</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>b8</b>) obtained with <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mo>+</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>c1</b>) difference between (<b>a2</b>) and (<b>b1</b>); (<b>c2</b>) difference between (<b>a2</b>) and (<b>b2</b>); (<b>c3</b>) difference between (<b>a2</b>) and (<b>b3</b>); (<b>c4</b>) difference between (<b>a2</b>) and (<b>b4</b>); (<b>c5</b>) difference between (<b>a2</b>) and (<b>b5</b>); (<b>c6</b>) difference between (<b>a2</b>) and (<b>b6</b>); (<b>c7</b>) difference between (<b>a2</b>) and (<b>b7</b>); (<b>c8</b>) difference between (<b>a2</b>) and (<b>b8</b>).</p>
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<p>Visual assessment of plaintext sensitivity for MIEA-PRHM: (<b>a1</b>) test image 2.1.05; (<b>b1</b>) the least significant pixel bit at (2,3) was modified; (<b>c1</b>) the least significant bit at (511,512) was modified; (<b>d1</b>) difference between (<b>a1</b>) and (<b>b1</b>); (<b>e1</b>) difference between (<b>a1</b>) and (<b>c1</b>); (<b>a2</b>) encrypted image of (<b>a1</b>); (<b>b2</b>) encrypted image of (<b>b1</b>); (<b>c2</b>) encrypted image of (<b>c1</b>); (<b>d2</b>) difference between (<b>a2</b>) and (<b>b2</b>); and (<b>e2</b>) difference between (<b>a2</b>) and (<b>c2</b>).</p>
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<p>Experimental results of pixel correlation assessment for MIEA-PRHM: in the <b>first column</b>, the test images 2.1.02 and 4.2.03, along with their encrypted images, are presented; the <b>second column</b> exhibits the 3D correlation analysis plots of the horizontal orientation for the images displayed in the <b>first column</b>; the <b>third column</b> showcases the correlation analysis plots of the vertical orientation; and the <b>final column</b> depicts the correlation analysis plots of the diagonal orientation.</p>
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<p>Experimental results of pixel distribution assessment for MIEA-PRHM: in the <b>first column</b>, the test images 4.2.06 and 4.2.07, along with their encrypted images, are presented; the <b>second column</b> exhibits the 3D pixel distribution plots of the red channels for the images displayed in the <b>first column</b>; the <b>third column</b> showcases the pixel distribution plots of green channels; and the <b>final column</b> depicts the pixel distribution plots of blue channels.</p>
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<p>Experimental results of MIEA-PRHM against noise attacks: the <b>first row</b> presents five contaminated ciphertext images, with salt-and-pepper noise intensities of 0.01, 0.02, 0.03, 0.04, and 0.05, respectively, added to each; the <b>second row</b> shows the corresponding decrypted images.</p>
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<p>Experimental results of MIEA-PRHM against data loss: The <b>first row</b> presents five ciphertext images with some pixels missing. The numbers of their missing pixels are <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>64</mn> <mo>×</mo> <mn>64</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>96</mn> <mo>×</mo> <mn>96</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>128</mn> <mo>×</mo> <mn>128</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>160</mn> <mo>×</mo> <mn>160</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math>, respectively. The <b>second row</b> shows the corresponding decrypted images.</p>
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24 pages, 15074 KiB  
Article
A Symmetric Reversible Audio Information Hiding Algorithm Using Matrix Embedding Within Image Carriers
by Yongqiang Tuo, Guodong Li and Kaiyue Hou
Symmetry 2024, 16(12), 1586; https://doi.org/10.3390/sym16121586 - 27 Nov 2024
Cited by 1 | Viewed by 674
Abstract
To address the vulnerability of existing hiding algorithms to differential attacks and the limitations of single chaotic systems, such as small key space and low security, a novel algorithm combining audio encryption with information hiding is proposed. First, the original audio is divided [...] Read more.
To address the vulnerability of existing hiding algorithms to differential attacks and the limitations of single chaotic systems, such as small key space and low security, a novel algorithm combining audio encryption with information hiding is proposed. First, the original audio is divided into blocks to enhance efficiency. A “one-time pad” mechanism is achieved by associating the key with the plaintext, and a new multidimensional sine-coupled chaotic map is designed, which, in conjunction with multiple chaotic systems, generates the key stream. Next, the block-processed audio signals are matrix-converted and then encrypted using cyclic remainder scrambling, an improved Josephus scrambling, XOR diffusion, and bit diffusion. This results in an encrypted audio information matrix. Finally, the GHM multiwavelet transform is used to select embedding channels, and the least significant bit (LSB) method is employed to hide the information within the carrier image. The algorithm is symmetric, and decryption involves simply reversing the encryption process on the stego image. Experimental results demonstrate that the Structural Similarity Index (SSIM) between the carrier image and the stego image is 0.992540, the Peak Signal-to-Noise Ratio (PSNR) is 49.659404 dB, and the Mean Squared Error (MSE) is 0.708044. These metrics indicate high statistical similarity and indistinguishability in visual appearance. The key space of the encryption algorithm is approximately 2850, which effectively resists brute-force attacks. The energy distribution of the encrypted audio approximates noise, with information entropy close to 8, uniform histograms, high scrambling degree, strong resistance to differential attacks, and robustness against noise and cropping attacks. Full article
(This article belongs to the Special Issue Algebraic Systems, Models and Applications)
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<p>Tent map phase diagram (<b>left</b>) and bifurcation and Lyapunov exponent plots of Tent chaos map (<b>right</b>).</p>
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<p>(<b>a</b>) Trajectories; (<b>b</b>) bifurcation diagrams; (<b>c</b>) Lyapunov exponents for multidimensional sine-coupled chaotic maps.</p>
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<p>Attractors in the <span class="html-italic">x</span>−<span class="html-italic">z</span> plane of the unified chaotic system and maximum Lyapunov exponent plots for varying parameters. (<b>a</b>) x-z Plane of the Unified Chaotic System with Parameter α= 0; (<b>b</b>) x-z Plane of the Unified Chaotic System with Parameter α = 0.6; (<b>c</b>) x-z Plane of the Unified Chaotic System with Parameter α = 0.8; (<b>d</b>) x-z Plane of the Unified Chaotic System with Parameter α = 1; (<b>e</b>) Maximum Lyapunov Exponent of the Unified Chaotic System with Varying Parameter α.</p>
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<p>Illustration of the enhanced Josephus permutation algorithm.</p>
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<p>Illustration of the enhanced Josephus permutation algorithm.</p>
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<p>Schematic diagram of the bit diffusion algorithm.</p>
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<p>Flowchart of audio encryption and steganography algorithms.</p>
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<p>Simulation experiment results. (<b>a</b>) Original audio time series; (<b>b</b>) Encrypted audio time series; (<b>c</b>) Carrier image; (<b>d</b>) Audio image; (<b>e</b>) Encrypted audio image; (<b>f</b>) Decrypted audio time Series.</p>
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<p>Original carrier image vs. carrier image with embedded encrypted audio. (<b>a</b>) Carrier image 1; (<b>b</b>) Carrier image 2; (<b>c</b>) Carrier image 3; (<b>d</b>) Carrier image 4; (<b>e</b>) Stego image 1; (<b>f</b>) Stego image 2; (<b>g</b>) Stego image 3; (<b>h</b>) Stego image 4.</p>
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<p>Histogram contrast: original carrier image vs. stego carrier with embedded data. (<b>a</b>) Histogram comparison of carrier image 1 before and after embedding; (<b>b</b>) Histogram comparison of carrier image 2 before and after embedding; (<b>c</b>) Histogram comparison of carrier image 3 before and after embedding; (<b>d</b>) Histogram comparison of carrier image 4 before and after embedding.</p>
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<p>Visualization of decryption outcomes using incorrect key.</p>
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<p>Spectrograms of original audio (<b>top</b>) and encrypted audio (<b>bottom</b>).</p>
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<p>Scatter plot of adjacent signal amplitudes for plaintext and encrypted audio. (<b>a</b>) Correlation plot of adjacent signal amplitudes for original audio 1; (<b>b</b>) Correlation plot of adjacent signal amplitudes for original audio 2; (<b>c</b>) Correlation plot of adjacent signal amplitudes for original audio 3; (<b>d</b>) Correlation plot of adjacent signal amplitudes for encrypted audio 1; (<b>e</b>) Correlation plot of adjacent signal amplitudes for encrypted audio 2; (<b>f</b>) Correlation plot of adjacent signal amplitudes for encrypted audio 3.</p>
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<p>Histograms of original audio (<b>left</b>) and encrypted audio (<b>right</b>).</p>
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<p>Scatter plot of permuted index distribution.</p>
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<p>Impact of noise attacks on hidden information robustness in steganography. (<b>a</b>) Stego image with 1% salt and pepper noise; (<b>b</b>) Stego image with 5% salt and pepper noise; (<b>c</b>) Stego image with 10% salt and pepper noise; (<b>d</b>) Decrypted audio from 1% salt and pepper noise; (<b>e</b>) Decrypted audio from 5% salt and pepper noise; (<b>f</b>) Decrypted audio from 10% salt and pepper noise.</p>
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<p>Results of cropping attack analysis on information hiding systems. (<b>a</b>) Stego image after 1% cropping attack; (<b>b</b>) Stego image after 5% cropping attack; (<b>c</b>) Stego image after 10% cropping attack; (<b>d</b>) Decrypted audio after 1% cropping attack; (<b>e</b>) Decrypted audio after 5% cropping attack; (<b>f</b>) Decrypted audio after 10% cropping attack.</p>
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17 pages, 5823 KiB  
Article
EADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of Things
by Zhiwei Si, Juhao Wang, Pengbiao Zhao, Xiaopei Wang and Jingcheng  Song
Sensors 2024, 24(22), 7162; https://doi.org/10.3390/s24227162 - 7 Nov 2024
Viewed by 809
Abstract
The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed [...] Read more.
The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed during the deployment of these SCs. Many scholars have adopted data aggregation to protect privacy, but these methods are difficult to achieve fine-grained data collection. To this end, this paper proposes an efficient anonymous data collection (EADC) scheme suitable for the IoT environment. The scheme combines matrix algorithms with homomorphic encryption (HE) technology to effectively cut off the connection between users and data during data upload. In addition, the EADC scheme introduces a sophisticated data grouping protocol to improve the overall efficiency of the system. Analysis shows that the scheme can achieve efficient data collection without compromising user privacy. Full article
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<p>The system model for EADC.</p>
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<p>Detailed introduction of EADC.</p>
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<p>Computational time of a single user at SS [<a href="#B30-sensors-24-07162" class="html-bibr">30</a>,<a href="#B37-sensors-24-07162" class="html-bibr">37</a>,<a href="#B38-sensors-24-07162" class="html-bibr">38</a>].</p>
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<p>Computational time of completing protocol at SS [<a href="#B30-sensors-24-07162" class="html-bibr">30</a>,<a href="#B37-sensors-24-07162" class="html-bibr">37</a>,<a href="#B38-sensors-24-07162" class="html-bibr">38</a>].</p>
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<p>Computation time for grouping at SS.</p>
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<p>Computation time during the data collection phase at SS [<a href="#B30-sensors-24-07162" class="html-bibr">30</a>,<a href="#B38-sensors-24-07162" class="html-bibr">38</a>,<a href="#B39-sensors-24-07162" class="html-bibr">39</a>,<a href="#B40-sensors-24-07162" class="html-bibr">40</a>].</p>
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<p>Communication time at SS [<a href="#B30-sensors-24-07162" class="html-bibr">30</a>,<a href="#B37-sensors-24-07162" class="html-bibr">37</a>,<a href="#B38-sensors-24-07162" class="html-bibr">38</a>].</p>
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<p>Communication time for grouping at SS.</p>
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<p>Throughput under different concurrent requests.</p>
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21 pages, 6346 KiB  
Article
Novel Steganographic Method Based on Hermitian Positive Definite Matrix and Weighted Moore–Penrose Inverses
by Selver Pepić, Muzafer Saračević, Aybeyan Selim, Darjan Karabašević, Marija Mojsilović, Amor Hasić and Pavle Brzaković
Appl. Sci. 2024, 14(22), 10174; https://doi.org/10.3390/app142210174 - 6 Nov 2024
Viewed by 754
Abstract
In this paper, we describe the concept of a new data-hiding technique for steganography in RGB images where a secret message is embedded in the blue layer of specific bytes. For increasing security, bytes are chosen randomly using a random square Hermitian positive [...] Read more.
In this paper, we describe the concept of a new data-hiding technique for steganography in RGB images where a secret message is embedded in the blue layer of specific bytes. For increasing security, bytes are chosen randomly using a random square Hermitian positive definite matrix, which is a stego-key. The proposed solution represents a very strong key since the number of variants of positive definite matrices of order 8 is huge. Implementing the proposed steganographic method consists of splitting a color image into its R, G, and B channels and implementing two segments, which take place in several phases. The first segment refers to embedding a secret message in the carrier (image or text) based on the unique absolute elements values of the Hermitian positive definite matrix. The second segment refers to extracting a hidden message based on a stego-key generated based on the Hermitian positive definite matrix elements. The objective of the data-hiding technique using a Hermitian positive definite matrix is to embed confidential or sensitive data within cover media (such as images, audio, or video) securely and imperceptibly; by doing so, the hidden data remain confidential and tamper-resistant while the cover media’s visual or auditory quality is maintained. Full article
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<p>Universal scenario for data embedding.</p>
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<p>Carrier image.</p>
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<p>R, G, and B channels of carrier.</p>
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<p>Base64 of the B channel.</p>
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<p>Binary of the B channel.</p>
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<p>Binary of the B channel with an embedded secret message.</p>
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<p>Base64 of the B channel with an embedded secret message.</p>
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<p>B channel with an embedded secret message.</p>
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<p>Original image with an embedded secret message.</p>
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<p>General scenario for data extraction.</p>
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<p>Histogram of the original B channel of the image.</p>
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<p>Histogram of the B channel of stego-image with a secret massage.</p>
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<p>The result of comparing histograms of the original and stego-images.</p>
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<p>Input parameters in the process of embedding data.</p>
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<p>Comparison of entropy (original vs. stego-image).</p>
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<p>Distribution of bits in the stego-image.</p>
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<p>Uniform distribution on R, G, and B channels in the stego-image.</p>
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<p>Detection percentage for 12 test cases for 5 types of attacks (series 1, 2, ...5).</p>
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22 pages, 7112 KiB  
Article
A New Encryption Algorithm Utilizing DNA Subsequence Operations for Color Images
by Saeed Mirzajani, Seyed Shahabeddin Moafimadani and Majid Roohi
AppliedMath 2024, 4(4), 1382-1403; https://doi.org/10.3390/appliedmath4040073 - 4 Nov 2024
Viewed by 936
Abstract
The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust [...] Read more.
The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image’s histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm’s NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability. Full article
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<p>DNA subsequence elongation and truncation processes.</p>
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<p>The schematic of the utilized procedure: (<b>a</b>) The schematic of the image encryption method, (<b>b</b>) The schematic of the decryption method.</p>
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<p>Encryption and decryption of images: (<b>a</b>–<b>d</b>): Plain images. (<b>e</b>–<b>h</b>): Respective encryption of images. (<b>i</b>–<b>l</b>): Respective decryption of images.</p>
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<p>(<b>a</b>) Original color image of Daryasar; (<b>b</b>–<b>d</b>) plain image histograms for R, G, and B, respectively; (<b>e</b>) cipher image; (<b>f</b>–<b>h</b>) cipher image histograms, respectively.</p>
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<p>Correlation histograms. (<b>a</b>,<b>c</b>,<b>e</b>) show the histograms for the original image, while (<b>b</b>,<b>d</b>,<b>f</b>) display the histograms for the encrypted image.</p>
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<p>Correlation histograms. (<b>a</b>,<b>c</b>,<b>e</b>) show the histograms for the original image, while (<b>b</b>,<b>d</b>,<b>f</b>) display the histograms for the encrypted image.</p>
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<p>Encrypted images with correct and incorrect initial keys, and their differences from the original encrypted images: (<b>a</b>–<b>e</b>) depict five newly encrypted images using the specified keys, while (<b>f</b>–<b>j</b>) illustrate the differences between the incorrectly encrypted images and the original image.</p>
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<p>Evaluation with selected plain images for uniform color patterns: (<b>a</b>) image with all-white pixels, (<b>b</b>) encrypted version of the all-white image, (<b>c</b>) histogram of the red channel for the all-white image, (<b>d</b>) image with all-black pixels, (<b>e</b>) encrypted version of the all-black image, (<b>f</b>) histogram of the red channel for the all-black image.</p>
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<p>Outcomes of the noise attack evaluation for the “Guangzhou” image ((<b>a</b>,<b>b</b>): 10% noise attack, (<b>c</b>,<b>d</b>): 15% noise attack, (<b>e</b>,<b>f</b>): 20% noise attack).</p>
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35 pages, 16066 KiB  
Article
Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption
by Ramalingam Sriraman and Ohmin Kwon
Mathematics 2024, 12(21), 3345; https://doi.org/10.3390/math12213345 - 25 Oct 2024
Viewed by 773
Abstract
With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be [...] Read more.
With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs. Full article
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Figure 1

Figure 1
<p>Transient behaviors of the states <inline-formula><mml:math id="mm451"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm452"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm453"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm454"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm455"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm456"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm457"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm458"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm459"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm460"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm461"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm462"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm463"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm464"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm465"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm466"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm467"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm468"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm469"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm470"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>1</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm471"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Transient behaviors of the states <inline-formula><mml:math id="mm472"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm473"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mn>2</mml:mn><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm474"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Phase diagram of the states <inline-formula><mml:math id="mm475"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm476"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm477"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Phase diagram of the states <inline-formula><mml:math id="mm478"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm479"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm480"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Phase diagram of the states <inline-formula><mml:math id="mm481"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm482"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm483"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Phase diagram of the states <inline-formula><mml:math id="mm484"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm485"><mml:semantics><mml:mrow><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm486"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Plots of the synchronization errors <inline-formula><mml:math id="mm487"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϱ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>0</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm488"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Plots of the synchronization errors <inline-formula><mml:math id="mm489"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϱ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm490"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Plots of the synchronization errors <inline-formula><mml:math id="mm491"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϱ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>2</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm492"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>Plots of the synchronization errors <inline-formula><mml:math id="mm493"><mml:semantics><mml:mrow><mml:msubsup><mml:mi>ϱ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msubsup><mml:mover accent="true"><mml:mi>ϑ</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msubsup><mml:mi>ϑ</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>{</mml:mo><mml:mn>3</mml:mn><mml:mo>}</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>(</mml:mo><mml:mi>ι</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> over time <inline-formula><mml:math id="mm494"><mml:semantics><mml:mi>ι</mml:mi></mml:semantics></mml:math></inline-formula> in Example 1.</p>
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<p>(<bold>a</bold>–<bold>c</bold>) corresponds to original, encrypted, and decrypted (using correct key) <inline-formula><mml:math id="mm495"><mml:semantics><mml:mrow><mml:mn>256</mml:mn><mml:mo>×</mml:mo><mml:mn>256</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> images of “Mandrill”. (<bold>d</bold>–<bold>f</bold>) corresponds to original, encrypted, and decrypted (using correct key) <inline-formula><mml:math id="mm496"><mml:semantics><mml:mrow><mml:mn>512</mml:mn><mml:mo>×</mml:mo><mml:mn>512</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> images of “Lion”. (<bold>g</bold>–<bold>i</bold>) corresponds to original, encrypted, and decrypted (using correct key) <inline-formula><mml:math id="mm497"><mml:semantics><mml:mrow><mml:mn>1024</mml:mn><mml:mo>×</mml:mo><mml:mn>1024</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> images of “Peppers”.</p>
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<p>The flow chart of encryption process.</p>
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<p>(<bold>a</bold>–<bold>c</bold>) corresponds to original, encrypted, and decrypted (using wrong key) <inline-formula><mml:math id="mm498"><mml:semantics><mml:mrow><mml:mn>256</mml:mn><mml:mo>×</mml:mo><mml:mn>256</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> image of “Mandrill”. (<bold>d</bold>–<bold>f</bold>) corresponds to original, encrypted, and decrypted (using wrong key) <inline-formula><mml:math id="mm499"><mml:semantics><mml:mrow><mml:mn>512</mml:mn><mml:mo>×</mml:mo><mml:mn>512</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> image of “Lion”. (<bold>g</bold>–<bold>i</bold>) corresponds to original, encrypted, and decrypted (using wrong key) <inline-formula><mml:math id="mm500"><mml:semantics><mml:mrow><mml:mn>1024</mml:mn><mml:mo>×</mml:mo><mml:mn>1024</mml:mn></mml:mrow></mml:semantics></mml:math></inline-formula> image of “Peppers”.</p>
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<p>The <inline-formula><mml:math id="mm501"><mml:semantics><mml:mrow><mml:mi mathvariant="script">R</mml:mi><mml:mi mathvariant="script">G</mml:mi><mml:mi mathvariant="script">B</mml:mi></mml:mrow></mml:semantics></mml:math></inline-formula> histogram of original image “Mandrill”.</p>
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<p>The <inline-formula><mml:math id="mm502"><mml:semantics><mml:mrow><mml:mi mathvariant="script">R</mml:mi><mml:mi mathvariant="script">G</mml:mi><mml:mi mathvariant="script">B</mml:mi></mml:mrow></mml:semantics></mml:math></inline-formula> histogram of shuffled image “Mandrill”.</p>
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<p>The <inline-formula><mml:math id="mm503"><mml:semantics><mml:mrow><mml:mi mathvariant="script">R</mml:mi><mml:mi mathvariant="script">G</mml:mi><mml:mi mathvariant="script">B</mml:mi></mml:mrow></mml:semantics></mml:math></inline-formula> histogram of encrypted image “Mandrill”.</p>
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<p>The first and second rows demonstrate the correlation analysis for original and encrypted image of “Mandrill” in red channel.</p>
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<p>The first and second rows demonstrate the correlation analysis for original and encrypted image of “Mandrill” in green channel.</p>
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<p>The first and second rows demonstrate the correlation analysis for original and encrypted image of “Mandrill” in blue channel.</p>
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21 pages, 34834 KiB  
Article
A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption
by Cemile İnce, Kenan İnce and Davut Hanbay
Entropy 2024, 26(10), 885; https://doi.org/10.3390/e26100885 - 21 Oct 2024
Viewed by 852
Abstract
As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and [...] Read more.
As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption. Full article
(This article belongs to the Section Complexity)
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<p>Complete block diagram of the proposed algorithm.</p>
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<p>Bifurcation and LE graphics of employed chaotic maps.</p>
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<p>Plaintext and relative cipher image histograms of SIPI dataset images.</p>
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<p>Three phase correlation graphics of SIPI dataset images employed in study.</p>
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<p>Three phase correlation graphics of SIPI dataset images employed in study continued.</p>
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<p>Full black and white image encryption, decryption and histogram results.</p>
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<p>Decryption result for different percentage data loss on cipher image.</p>
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<p>Decryption result for different percentage Salt and Peppers noise on cipher image.</p>
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20 pages, 11976 KiB  
Article
Synchronization of Chaotic Extremum-Coded Random Number Generators and Its Application to Segmented Image Encryption
by Shunsuke Araki, Ji-Han Wu and Jun-Juh Yan
Mathematics 2024, 12(19), 2983; https://doi.org/10.3390/math12192983 - 25 Sep 2024
Viewed by 663
Abstract
This paper proposes a highly secure image encryption technique based on chaotic synchronization. Firstly, through the design of a synchronization controller, we ensure that the master–slave chaotic extremum-coded random number generators (ECRNGs) embedded in separated transmitters and receivers are fully synchronized to provide [...] Read more.
This paper proposes a highly secure image encryption technique based on chaotic synchronization. Firstly, through the design of a synchronization controller, we ensure that the master–slave chaotic extremum-coded random number generators (ECRNGs) embedded in separated transmitters and receivers are fully synchronized to provide synchronized dynamic random sequences for image encryption applications. Next, combining these synchronized chaotic sequences with the AES encryption algorithm, we propose an image segmentation and multi-encryption method to enhance the security of encrypted images and realize a secure image transmission system. Notably, in the design of the synchronization controller, the transient time before complete synchronization between the master and slave ECRNGs is effectively controlled by specifying the eigenvalues of the matrix in the synchronization error dynamics. Research results in this paper also show that complete synchronization of ECRNGs can be achieved within a single sampling time, which significantly contributes to the time efficiency of the image transmission system. As for the image encryption technique, we propose the method of image segmentation and use the synchronized dynamic random sequences generated by the ECRNGs to produce the keys and initialization vectors (IVs) required for AES-CBC image encryption, greatly enhancing the security of the encrypted images. To highlight the contribution of the proposed segmented image encryption, statistical analyses are conducted on the encrypted images, including histogram analysis (HA), information entropy (IE), correlation coefficient analysis (CCA), number of pixels change rate (NPCR), and unified average changing intensity (UACI), and compared with existing literature. The comparative results fully demonstrate that the proposed encryption method significantly enhances image encryption performance. Finally, under the network transmission control protocol (TCP), the synchronization of ECRNGs, dynamic keys, and IVs is implemented as well as segmented image encryption and transmission, and a highly secure image transmission system is realized to validate the practicality and feasibility of our design. Full article
(This article belongs to the Special Issue New Advances in Coding Theory and Cryptography, 2nd Edition)
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<p>The architecture of the image secure transmission system.</p>
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<p>State responses of master–slave systems (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mtable> <mtr> <mtd> <mn>0.5</mn> </mtd> <mtd> <mn>0.5</mn> </mtd> <mtd> <mtable> <mtr> <mtd> <mn>0.5</mn> </mtd> <mtd> <mo>−</mo> <mn>0.4</mn> </mtd> </mtr> </mtable> </mtd> </mtr> </mtable> </mrow> </mfenced> </mrow> </semantics></math>, (<b>b</b>)<math display="inline"><semantics> <mrow> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> </mtable> </mtd> </mtr> </mtable> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>Image segmentation encryption.</p>
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<p>Histogram analysis of 256 × 256 Lena with different pairs of keys and IVs; (<b>a</b>) original image (<b>b</b>) 1 pair, (<b>c</b>) 4 pairs, (<b>d</b>) 16 pairs.</p>
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<p>Histogram analysis of 512 × 512 Lena with different pairs of keys and IVs; (<b>a</b>) original image, (<b>b</b>) 1 pair, (<b>c</b>) 4 pairs, (<b>d</b>) 16 pairs.</p>
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<p>CCA analysis of 256 × 256 Lena with different pairs of keys and IVs; (<b>a</b>) original image, (<b>b</b>) 1 pair, (<b>c</b>) 4 pairs, (<b>d</b>) 16 pairs.</p>
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<p>CCA analysis of 512 × 512 Lena with different pairs of keys and IVs; (<b>a</b>) original image, (<b>b</b>) 1 pair, (<b>c</b>) 4 pairs, (<b>d</b>) 16 pairs.</p>
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<p>The flowchart of the high-security image transmission system.</p>
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<p>The completed system and execution interface; (<b>a</b>) transmitter, (<b>b</b>) receiver.</p>
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16 pages, 7263 KiB  
Article
Asymmetric Optical Scanning Holography Encryption with Elgamal Algorithm
by Chunying Wu, Yinggang Ding, Aimin Yan, Ting-Chung Poon and Peter Wai Ming Tsang
Photonics 2024, 11(9), 878; https://doi.org/10.3390/photonics11090878 - 19 Sep 2024
Cited by 1 | Viewed by 1099
Abstract
This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private [...] Read more.
This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private key and the receiver’s public key. The resulting ciphertext matrices are then transmitted to the receiver. Upon receipt, the receiver decrypts the ciphertext matrices using their private key and the sender’s public key. We employ an asymmetric single-image encryption method for key management and dispatch for securing imaging and transmission. Furthermore, we conducted a sensitivity analysis of the encryption system. The image encryption metrics, including histograms of holograms, adjacent pixel correlation, image correlation, the peak signal-to-noise ratio, and the structural similarity index, were also examined. The results demonstrate the security and stability of the proposed method. Full article
(This article belongs to the Special Issue Holographic Information Processing)
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<p>Diagram of optical scanning holographic system. <math display="inline"><semantics> <mrow> <mi>B</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>: beam splitter; <math display="inline"><semantics> <mrow> <mi>AOM</mi> </mrow> </semantics></math>: acousto-optic modulator; <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> <mo>:</mo> </mrow> </semantics></math> silver mirrors; <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> <mo>:</mo> </mrow> </semantics></math> Fourier lenses; <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>3</mn> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <msub> <mi>L</mi> <mn>4</mn> </msub> <mo>:</mo> <mo> </mo> <mi>lenses</mi> </mrow> </semantics></math> for collecting light energy; <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>D</mi> </mrow> </semantics></math> and <span class="html-italic">PD</span><sub>1</sub>: photo-detectors; <math display="inline"><semantics> <mrow> <mi>BPF</mi> <mo>:</mo> <mrow> <mo> </mo> <mi>band</mi> </mrow> <mo>−</mo> <mrow> <mi>pass</mi> <mo> </mo> <mi>filter</mi> <mo> </mo> <mi>tuned</mi> <mo> </mo> <mi>at</mi> <mo> </mo> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mo>;</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>LPF</mi> </mrow> </semantics></math>: low-pass filter; <math display="inline"><semantics> <mrow> <mi>ADC</mi> </mrow> </semantics></math>: analog-to-digital converter; <math display="inline"><semantics> <mrow> <mi>PC</mi> </mrow> </semantics></math>: digital computer.</p>
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<p>A simplified Elgamal encryption algorithm as part of our proposed method.</p>
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<p>Our proposed three-round handshake Elgamal encryption algorithm.</p>
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<p>Our proposed OSH system with Elgamal encryption.</p>
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<p>(<b>a</b>) Four-sheep original image. (<b>b</b>) Cosine hologram. (<b>c</b>) Sine hologram.</p>
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<p>(<b>a</b>) Cosine sign ciphertext. (<b>b</b>) Unsigned cosine fractional XOR ciphertext. (<b>c</b>) Sine sign ciphertext. (<b>d</b>) Unsigned sine fractional XOR ciphertext.</p>
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<p>The reconstructed (<b>a</b>) cosine hologram and (<b>b</b>) sine hologram. (<b>c</b>) The decrypted image.</p>
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<p>Histograms of the (<b>a</b>) cosine hologram, (<b>b</b>) unsigned cosine fractional XOR ciphertext, (<b>c</b>) cosine sign ciphertext, (<b>d</b>) sine hologram, (<b>e</b>) unsigned sine fractional XOR ciphertext, and (<b>f</b>) sine sign ciphertext.</p>
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<p>Adjacent pixel distributions in the horizontal, vertical, and diagonal directions of (<b>a</b>–<b>c</b>) the cosine hologram and (<b>d</b>–<b>f</b>) the sine hologram.</p>
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<p>Adjacent pixel distributions in the horizontal, vertical, and diagonal directions of (<b>a</b>–<b>c</b>) the cosine sign ciphertext and (<b>d</b>–<b>f</b>) the sine sign ciphertext.</p>
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<p>Adjacent pixel distributions in the horizontal, vertical, and diagonal directions of (<b>a</b>–<b>c</b>) the cosine sign ciphertext and (<b>d</b>–<b>f</b>) the sine sign ciphertext.</p>
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<p>Salt-and-pepper noise attack analysis: (<b>a</b>) decrypted images with noise intensity with variance of 0.01; (<b>b</b>) decrypted results with noise intensity with variance of 0.05.</p>
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