Joint Steganography and Encryption Techniques for Security Enhancement Using Deep Learning Models
Abstract
In today’s rapidly expanding digital landscape, safeguarding sensitive data and privacy has become imperative. Traditional security measures and authentication technologies are facing increasing challenges, particularly steganography. Steganography involves concealing messages within various forms of media, such as images, audio, or video, to avoid detection by third parties. Unfortunately, conventional steganography techniques are vulnerable to advanced decryption methods, emphasizing the urgent need for more robust strategies. The proposed scheme introduces an innovative approach to conceal secrets through adversarial reinforcement and diffusion models. In this model, the secret image transforms noises, and an inverse model is designed to revert the noise to the original secret image. Experimental results demonstrate the feasibility and effectiveness of the proposed scheme.
References
[1]
Nasir Ahmed, T_ Natarajan, and Kamisetty R Rao. 1974. Discrete cosine transform. IEEE transactions on Computers 100, 1 (1974), 90–93.
[2]
Shumeet Baluja. 2017. Hiding Images in Plain Sight: Deep Steganography. In Neural Information Processing Systems. http://www.esprockets.com/papers/nips2017.pdf
[3]
VV Jadhav and PP Belagali. 2016. An effective Image steganography using LSB Matching Revisited. vol 5 (2016), 15–19.
[4]
Jie Luo, Peisong He, Jiayong Liu, Hongxia Wang, Chunwang Wu, Yijing Chen, Wanjie Li, and Jiangchuan Li. 2023. Content-adaptive Adversarial Embedding for Image Steganography Using Deep Reinforcement Learning. In 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 49–54.
[5]
Jarno Mielikainen. 2006. LSB matching revisited. IEEE signal processing letters 13, 5 (2006), 285–287.
[6]
Kazem Qazanfari and Reza Safabakhsh. 2017. An improvement on LSB matching and LSB matching revisited steganography methods. arXiv preprint arXiv:1709.06727 (2017).
[7]
Tamer Rabie and Ibrahim Kamel. 2017. High-capacity steganography: a global-adaptive-region discrete cosine transform approach. Multimedia Tools and Applications 76 (2017), 6473–6493.
[8]
Filip Radenović, Giorgos Tolias, and Ondřej Chum. 2018. Fine-tuning CNN image retrieval with no human annotation. IEEE transactions on pattern analysis and machine intelligence 41, 7 (2018), 1655–1668.
[9]
Saeed Sarreshtedari, Mohsen Ghotbi, and Shahrokh Ghaemmaghami. 2009. One-third probability embedding: Less detectable LSB steganography. In 2009 IEEE International Conference on Multimedia and Expo. IEEE, 1002–1005.
[10]
Mark J Shensa 1992. The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Transactions on signal processing 40, 10 (1992), 2464–2482.
[11]
Abdelfatah A Tamimi, Ayman M Abdalla, and Omaima Al-Allaf. 2013. Hiding an image inside another image using variable-rate steganography. International Journal of Advanced Computer Science and Applications (IJACSA) 4, 10 (2013).
[12]
Weixuan Tang, Bin Li, Mauro Barni, Jin Li, and Jiwu Huang. 2020. An automatic cost learning framework for image steganography using deep reinforcement learning. IEEE Transactions on Information Forensics and Security 16 (2020), 952–967.
[13]
Amirhossein Tavanaei. 2020. Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI. arxiv:2007.06712 [cs.CV]
[14]
Andrew B Watson. 1993. DCT quantization matrices visually optimized for individual images. In Human vision, visual processing, and digital display IV, Vol. 1913. SPIE, 202–216.
[15]
Pin Wu, Yang Yang, and Xiaoqiang Li. 2018. Stegnet: Mega image steganography capacity with deep convolutional network. Future Internet 10, 6 (2018), 54.
[16]
Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, and Kalyan Veeramachaneni. 2019. SteganoGAN: High capacity image steganography with GANs. arXiv preprint arXiv:1901.03892 (2019).
[17]
Ru Zhang, Shiqi Dong, and Jianyi Liu. 2019. Invisible steganography via generative adversarial networks. Multimedia tools and applications 78 (2019), 8559–8575.
[18]
Xinpeng Zhang and Shuozhong Wang. 2006. Efficient steganographic embedding by exploiting modification direction. IEEE Communications letters 10, 11 (2006), 781–783.
Index Terms
- Joint Steganography and Encryption Techniques for Security Enhancement Using Deep Learning Models
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Published In
March 2024
229 pages
ISBN:9798400716829
DOI:10.1145/3655755
Copyright © 2024 ACM.
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Published: 08 June 2024
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- Kaohsiung Chang Gung Memorial Hospital
- National Science and Technology Council of the Republic of China, Taiwan
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IVSP 2024
IVSP 2024: 2024 6th International Conference on Image, Video and Signal Processing
March 14 - 16, 2024
Ikuta, Japan
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