Abstract
Image authentication techniques have recently gained great attention due to its importance for a large number of multimedia applications. Digital images are increasingly transmitted over non-secure channels such as the Internet. Therefore, military, medical and quality control images must be protected against attempts to manipulate them; such manipulations could tamper the decisions based on these im- ages. To protect the authenticity of multimedia images, there are several approaches including conventional cryptography, fragile and semi-fragile watermarking and dig- ital signatures that are based on the image content. The aim of this paper is to present a review on different Machine learning techniques as Fuzzy Set Theory, Rough Set Theory, Rough K-means clustering, Near Sets and Nearness Approximation Spaces, Vector quantization, Genetic Algorithm, Particle Swarm Optimization, Support Vec- tor Machine and applying them in image authentication.
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Haouzia, A., & Noumeir, R. (2008). Methods for image authentication: a survey. Multimedia Tools and Applications, 39, 1–46.
Lin, C., & Hsieh, W. (2003). Applying Projection and B-spline to Image Authentication and Remedy. IEEE Transactions on Consumer Electronics, 49, 1234–1239.
Chen, N., Feng, B., Wang, Z., & Zhang, H. (2005). Hedging Uncertainty in Rough Set-based Approach with Fuzzy Decision. Information Technology Journal, 4, 387–390.
Lu, T., Chang, C., & Liu, Y. (2006). A Content-Based Image Authentication Scheme Based on Singular Value Decomposition. Pattern Recognition and Image Analysis, 16, 506–522.
Gao, T., Gu, Q., & Emmanuel, S. (2009). A novel image authentication scheme based on hyper-chaotic cell neural network. Chaos, Solitons and Fractals, 42, 548–553.
Wang, S., & Tsai, S. (2008). Automatic image authentication and recovery using fractal code embedding and image inpainting. Pattern Recognition Society, 41, 701–712.
Zhang, F., Zhang, X., & Chen, Z. (2005). Digital Image Authentication Based on Error-Correction Codes. Computational Intelligence and Security, 3802, 433–438.
Zhang, H., Yang, C., & Quan, X. (2004). Image Authentication Based on Digital Signature and Semi-Fragile Watermarking. the journal of Computer Science and Technology, 19, 752–759.
Tang, Y., & Chen, C. (2006). Image Authentication Using Hierarchical Semi-Fragile Watermarks. E-Business and Telecommunication Networks, 4, 241–246.
Hung, K., & Chang, C. (2007). Recoverable Tamper Proofing Technique for Image Authentication Using Ir regular Sampling Coding. Lecture Notes in Computer Science, 4610, 333–343.
Puhan, N., & Ho, A. (2005). Secure Tamper Localization in Binary Document Image Authentication. Lecture Notes in Computer Science, 3684, 263–271.
Ye, S., Sun, Q., & Chang, E. (2007). Statistics- and Spatiality-Based Feature Distance Measure for Error Re silient Image Authentication. Lecture Notes in Computer Science, 4499, 48–67.
Zhang, J., Liu, F., Wang, P., & Wang, G. (2007). Value Combination Technique for Image Authentication. Lecture Notes in Computer Science, 4810, 276–285.
Feng, W., & Liu, Z. (2008). Region-Level Image Authentication Using Bayesian Structural Content Abstraction. IEEE Transactions On Image Processing, 17, 2413–2424.
Ma, K. (2007). Machine Learning to Boost the Next Generation of Visualization Technology. IEEE Computer Society, 27, 6–9. doi:10.1109/MCG.2007.129.
Chalup, S., Craig, L., Murch, C., & Quinlan, M. (2007). Machine Learning With AIBO Robots in the Four-Legged League of Robocop. IEEE Transactions On Systems, Man, and Cybernetics, 37, 297–310.
Kotsiantis, S., Zaharakis, I., & Pintelas, P. (2007). Machine learning: a review of classification and combining techniques. Science and Business, 26, 159–190.
Rodriguez, B., Perez, O., Garcia, J., & Molina, J. (2005). Application of Machine Learning Techniques for Simplifying the Association Problem in a Video Surveillance System. Lecture Notes in Computer Science, 3562, 509–518.
Kurgan, L., & Cios, K. (2004). CAIM Discretization Algorithm. IEEE Transactions On Knowledge and Data Engineering, 16, 145–153.
Hua, J. (2008). Study on the Application of Rough Sets Theory in Machine Learning. IEEE Second Interna tional Symposium on Intelligent Information Technology Application, 1, 192–196.
Park, B., Won, Y., Choi, M., Kim, M., & Hong, J. (2008). Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning, Proceedings of the 11th AsiaPacific Symposium on Network Operations and Management Challenges for Next Generation Network Operations and Service Management. Lecture Notes in Computer Science, 5297, 474–477.
Thulin, J., Master’s Thesis in Computer Science: Machine learning-based classifiers in the Direkt Profil grammatical profiling system, pp. 1–67, 2007.
Tsai, C., & Chen, M. (2010). Credit rating by hybrid machine learning techniques. Applied Soft Computing, 10, 1–7.
Meyfroidt, G., Guiza, F., Ramon, J., & Bruynooghe, M. (2008). Machine learning techniques to examine large patient Databases. Best Practice and Research Clinical Anaesthesiology, 23, 127–143.
Jin, Y., & Sendhoff, B. (2008). Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions On Systems, Man, and Cybernetics, 38, 397–415.
Cheng, J., Tegge, A., & Baldi, P. (2008). Machine Learning Methods for Protein Structure Prediction. IEEE Reviews in Biomedical Engineering, 1, 41–49.
Tistarelli, M., & Grosso, E. (2000). Active vision-based face authentication. Image and Vision Computing, 18, 299–314.
Chun-hua, L., He-fei, L., & Zheng-ding, L. (2007). Semi-Fragile Watermarking Based on SVM for Image Authentication. Proceedings of IEEE International Conference on Multimedia and Expo, 10, 1255–1258.
Chan, K., Lee, T., Sample, P., Goldbaum, M., Weinreb, R., & Sejnowski, T. (2002). Comparison of Machine Learning and Traditional Classifiers in Glaucoma Diagnosis. IEEE Transactions On Biomedical En- gineering, 49, 963–974.
Lai, C. (2007). An empirical study of three machine learning methods for spam filtering. Knowledge-Based-Systems, 20, 249–254.
Mitra, S., & Banka, H. (2007). Application of Rough Sets in Pattern Recognition, Transactions on Rough Sets VII. Lecture Notes in Computer Science, 4400, 151–169.
Yamaguchil, D., Li, G., & Nagai, M. (2006). On the Combination of Rough Set Theory and Grey Theory Based on Grey Lattice Operations, Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science, 4259, 507–516.
Rebolledo, M. (2006). Rough intervals-enhancing intervals for qualitative modeling of technical systems. Artificial Intelligence, 170, 667–685.
Parmar, D., Wu, T., & Blackhurst, J. (2007). MMR: An algorithm for clustering categorical data using Rough Set Theory. Data and Knowledge Engineering, 63, 879–893.
Kumar, P., Krishna, P., Bapi, R., & De, S. (2007). Rough clustering of sequential data. Data and Knowledge Engineering, 63, 183–199.
Gao, Q., Gao, X., & Hu, Y. (2009). A new fuzzy set theory satisfying all classical set formulas. Computer Science and Technology, 4, 798–804.
Tao, F., Zhao, D., & Zhang, L. (2009). Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowledge Information System, 6, 1–24.
Monjezi, M., Rezaei, M., & Yazdian, A. (2009). Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Systems with Applications, 14, 1–7.
Hamidi, J., Shahriar, K., Rezai, B., & Bejari, H. (2009). Application of Fuzzy Set Theory to Rock Engineering Classification Systems: An Illustration of the Rock Mass Excavability Index. Rock Mechanics and Rock Engineering, 43, 1–16.
Lee, Z., Lin, S., Su, S., & Lin, C. (2008). A hybrid watermarking technique applied to digital images. Applied Soft Computing, 8, 798–808.
Lee, Z., Su, S., & Lee, C. (2003). Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Transactions On Systems, Man, and Cybernetics, 33, 113–121.
Lee, Z., Wang, Y., & Su, S. (2004). A genetic algorithm based robust learning credit assignment cerebellar model articulation controller. Applied Soft Computing, 4, 357–367.
Chang, T., & Chien, Y. (2007). The application of genetic algorithm in debris flows prediction. Environmental Geology, 53, 339–347.
Paszkowicz, W. (2006). Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing. Analytica Chim- ica Acta, 566, 81–98.
Jung, H., Song, I., & Jeong, B. (2007). Genetic algorithm-based integrated production planning considering manufacturing partners. The International Journal of Advanced Manufacturing Technology, 32, 547–556.
Cui, X., Potok, T., & Palathingal, P. (2005). Document Clustering using Particle Swarm Optimization. the IEEE Swarm Intelligence Symposium USA, 10, 185–191.
Huang, H., & Kim, K. (2006). Unsupervised Clustering Analysis of Gene Expression. Chance, 19, 1–7.
Li-ping, Z., Huan-jun, Y., & Shang-xu, H. (2005). Optimal Choice of Parameters for Particle Swarm Optimization. Journal of Zhejiang University, science A, 6, 528–534.
Meissner, M., Schmuker, M., & Schneider, G. (2006). Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics, 7, 125–136.
Shi, Y. (2004). Particle Swarm Optimization. Proceedings IEEE International Conference on Neural Networks Society, IV, 1942–1948.
Shigei, N., Miyajima, H., Maeda, M., & Mac, L. (2009). Bagging and AdaBoost algorithms for vector quantization. Neurocomputing, 73, 106–114.
Campobello, G., Mantineo, M., Patane, G., & Russo, M. (2005). LBGS: A smart approach for very large data sets vector quantization. Signal Processing, Image Communication, 20, 91–114.
Bodt, E., Cottrell, M., Letremy, P., & Verleysen, M. (2004). On the use of self-organizing maps to accelerate vector quantization. Neurocomputing, 56, 187–203.
Zhang, X., Guan, Z., & Gan, T. (2007). Particle Swarm Optimization Applied to Image Vector Quantization, Life System Modeling and Simulation. Lecture Notes in Computer Science, 4689, 507–515.
Holland, J., Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, Vol. 10, pp. 1–228, 1992.
Kumsawat, P., Attkitmongco, K., Srikaew, A., & Sujitjorn, S. (2004). Wavelet- Based Image Watermarking Using the Genetic Algorithm, Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science, 3215, 643–649.
Kumsawat, P., Attakitmongcol, K., & Srikaew, A. (2005). A New Approach for Optimization in Image Water marking by Using Genetic Algorithms. IEEE Transactions on Signal Processing, 12, 4707–4719.
Wang, F., Pan, J., Jain, L., & Huang, H. (2004). VQ-Based Gray Watermark Hiding Scheme and Genetic Index Assigment, Advances in Multimedia Information Processing. Lecture Notes in Computer Science, 3332, 73–80.
Shieha, C., Huang, H., Wang, F., & Pana, J. (2004). Genetic Watermarking Based on Transform-domain Techniques. Pattern Recognition Society, 3, 555–565.
Chang, Y., Sun, K., & Chen, Y. (2005). ART2-Based Genetic Watermarking. Advanced Information Networking and Applications, 1, 729–734.
Aslantas, V. (2008). A Singular-value Decomposition-based Image Watermarking Using Genetic Algorithm. International Journal of Electronics and Communications, 62, 386–394.
Aslantas, V., Ozer, S., & Ozturk, S. (2007). A Novel Clonal Selection Algorithm Based Fragile Watermarking Method, Artificial Immune Systems. Lecture Notes in Computer Science, 4628, 358–369.
Yongqiang, C., Yanqing, Z., Lihua, P., A DWT Domain Image Watermarking Scheme Using Genetic Algorithm and Synergetic Neural Network, Proceedings of the 2009 International Symposium on Information Processing (ISIP'09), pp. 298–301, 2009.
Wang, Z., Sun, X., & Zhang, D. (2007). A Novel Watermarking Scheme Based on PSO Algorithm, Bio Inspired Computational Intelligence and Applications. Lecture Notes in Computer Science, 4688, 307–314.
Aslantas, V., Ozer, S., & Ozturk, S. (2009). Improving the performance of DCT- Based fragile watermarking using intelligent optimization algorithms. Optics Communications, 282, 2806–2817.
Lu, Z., Pan, J., & Sun, S. (2000). VQ-based digital image watermarking Method. IEE Electronic Letters, 14, 1192–1201.
Makur, A., & Selvi, S. (2001). Variable dimension vector quantization based image watermarking. Signal Processing, 81, 93–889.
Wu, H., & Chang, C. (2005). A novel digital image watermarking scheme based on the vector quantization technique. Computers and Security, 24, 460–471.
Lu, Z., Liu, C., Xu, D., & Sun, S. (2003). Semi-fragile image watermarking method based on index constrained vector quantization. Electronics Letters, 39, 35–36.
Wu, H., Yeh, C., & Tsai, C. (2006). A Semi-fragile Watermarking Scheme Based on SVD and VQ Techniques, Computational Science and Its Applications. Lecture Notes in Computer Science, 3982, 406–415.
Chang, C., Tsou, C., & Chou, Y. (2007). A Remediable Image Authentication Scheme Based on Feature Extraction and Clustered VQ, Advances in Multimedia Information Processing. Lecture Notes in Computer Science, 4810, 446–449.
Lu, Z., Burkhardt, H., & Chu, S. (2007). Multipurpose Image Watermarking Algorithms and Applications. Studies in Computational Intelligence, 58, 287–323.
Chen, N., & Zhu, J. (2008). Multipurpose audio watermarking algorithm. Journal of Zhejiang University SCIENCE A, 4, 517–523.
Yang, C., & Shen, J. (2009). Recover the tampered image based on VQ indexing. Signal Processing, 90, 331–343.
Wong, P., & Memon, N. (2001). Secret an public key image watermarking schemes for image authentication and ownership verification. IEEE Transactions On Image Processing, 10, 1593–1601.
Lee, T., & Lin, S. (2008). Dual watermark for image tamper detection and recovery. Pattern Recognition, 41, 3497–3506.
Shen, J., & Ren, J. (2009). A robust associative watermarking technique based on vector Quantization. Digital Signal Processing, 20, 1–16.
Yong-Gang, F., Rui-Min, S., & Hong-Tao, L. (2004). Watermarking scheme based on support vector machine for color images. IEE Electronics Letters, 16, 986–987.
Jian-Zhen, W., & Jian-Ying, X. (2006). An adaptive watermarking scheme utilizing support vector machine for synchronization. Journal of Shanghai Jiaotong University, 3, 481–484.
Tsai, H., & Sun, D. (2007). Color image watermark extraction based on support vector machines. Information Sciences, 2, 550–569.
Wang, X., Xu, Z., & Yang, H. (2009). A robust image watermarking algorithm using SVR detection. Expert Systems with Applications, 36, 9056–9064.
Park, K. (2006). Robust Fake Iris Detection. Lecture Notes in Computer Science, 4069, 10–18.
Park, K., Whang, M., Lim, J., & Cho, Y. (2006). Fake Iris Detection Based on Multiple Wavelet Filters and Hierarchical SVM, Information Security and Cryptology. Lecture Notes in Computer Science, 4296, 246–256.
Wang, X., Yang, H., & Cui, C. (2008). An SVM-based robust digital image watermarking against desynchronization attacks. Signal Processing, 2193.
Singh, R., Vatsa, M., Singh, S., & Upadhyay, S. (2009). Integrating SVM Classification with SVD watermarking for intelligent video authentication. Telecommun System, 40, 5–15.
Singh, R., Vatsa, M., Singh, S., & Upadhyay, S. (2008). Video Authentication Using Relative Correlation Information and SVM. Studies in Computational Intelligence, 96, 511–529.
Lou, C., Yin, T., Chang, M., Robust Digital Watermarking Using Fuzzy Inference Technique, JOURNAL OF C.C.I.T., VOL. 32, 2004.
Qiong, W., Shaojie, S., Wei, Z., & Guohui, L. (2009). Identification of Inpainted Images and Natural Images for Digital Forensics. Journal of Electronics, 26, 341–345.
Lingras, P. (2007). Applications of rough set based K-means, Kohonen SOM, GA clustering, Transactions on Rough Sets. Lecture Notes in Computer Science, 2, 120–139.
Lingras, P. (2004). Interval set clustering of web users with rough K-Means. Journal of Intelligent Information Systems, 23, 5–16.
Pawlak, Z. (1984). On Rough Sets. Bulletin of the European Association for Theoretical Computer Science, 24, 94–109.
Hassanien, A., Milanova, M., Smolinsk, T., & Abraham, A. (2008). Computational intelligence in solving bioinformatics problems, Reviews, Perspectives, and Challenges. Computational Intelligence in Solv- ing Bioinformatics Problems, 151, 3–47.
Lin, C., & Chang, S. (1998). A robust image authentication method surviving JPEG lossy compression, Proceedings of SPI Einternational conference on storage and retrieval of image, video database. Proceedings of SPI Einternational conference on storage and retrieval of image, video database, 3312, 296–307.
Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. In proceedings of the IEEE International Conference on Neural Networks, IV, 1942–1948.
Hassanien, A., Abraham, A., Peters, J.F., and Kacprzyk, J., Rough sets in medical imaging: foundations and trends, Computational Intelligence in Medical Imaging: Techniques and Applications, G. Schaefer et al. (Eds.), CRC Press, USA, ISBN 978-1-4200-6059-1, Chapter 3, pp. 47–87, 2008.
Hassanien, A., Abraham, A., Kacprzyk, J., & Peters, J. F. (2008). Computational Intelligence in Multimedia Processing: Foundation and Trends. Studies in Computational Intelligence, 96, 3–49.
Peters, J. F. (2009). Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox, Foundations of Computational Intelligence. Studies in Computational Intelligence, 2, 3–25.
Peters, J. F. (2007). Near Sets. General Theory About Nearness of Objects. Applied Mathematical Sciences, 1, 2609–2629.
Peters, J. F., & Ramanna, S. (2008). Feature Selection: Near Set Approach, Mining Complex Data. Lecture Notes in Computer Science, 4944, 57–71.
Skowron, A., & Stepaniuk, J. (1995). Generalized approximation spaces, Soft Computing (pp. 18–21). San Diego: Simulation Councils.
El Bakrawy, L., Ghali, N., Hassanein, A., & Peters, J. F. (2011). Strict Authentication of Multimodal Biometric Images Using Near Sets. Soft Computing in Industrial Applications Advances in Intelligent and Soft Computing, 96, 249–258.
El Bakrawy, L., Ghali, N., Hassanein, A., Kim, T., A Rough K-means Fragile Watermarking Approach for Image Authentication, Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 19–23, 2011.
Bhandari, D., Lopamudra Kundu, L., & Pal, S. (2011). Optimal Parameter Selection for Image Watermarking Using MOGA. Pattern Recognition and Machine Intelligence, 6744, 280–285.
Wang, Y., Lin, W., & Yang, L. (2011). An intelligent watermarking method based on particle swarm optimization. Expert Systems with Applications, 38, 8024–8029.
Lin, H., Horng, J., Kao, W., Fan, P., Lee, L., & Pan, Y. (2008). An efficient watermarking method based on significant difference of wavelet coefficient quantization. IEEE Transactions on Multimedia, 10(5), 746–757.
Tsai, H., Tseng, H., & Lai, Y. (1965). Robust lossless image watermarking based on a-trimmed mean algorithm and support vector machine, The Journal of Systems and Software, Vol. 83, pp. 1015–1028, 2010. 115. Zadeh L A, Fuzzy sets. Information and Control, 8, 338–353.
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El Bakrawy, L.M., Ghali, N.I. & ella Hassanien, A. Intelligent Machine Learning in Image Authentication. J Sign Process Syst 78, 223–237 (2015). https://doi.org/10.1007/s11265-013-0817-4
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DOI: https://doi.org/10.1007/s11265-013-0817-4