Affiliation:
1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Abstract
Deep neural networks have achieved remarkable success in various fields of artificial intelligence. However, these models, which contain a large number of parameters, are widely distributed and disseminated by researchers, engineers, and even unauthorized users. Except for intelligent tasks, typically overparameterized deep neural networks have become new digital covers for data hiding, which may pose significant security challenges to AI systems. To address this issue, this paper proposes a symmetric steganalysis scheme specifically designed for neural networks trained for image classification tasks. The proposed method focuses on detecting the presence of additional data without access to the internal structure or parameters of the host network. It employs a well-designed method based on histogram distribution to find the optimal decision threshold, with a symmetric determining rule where the original networks and stego networks undergo two highly symmetrical flows to generate the classification labels; the method has been shown to be practical and effective. SVM and ensemble classifiers were chosen as the binary classifier for their applicability to feature vectors output from neural networks based on different datasets and network structures. This scheme is the first of its kind, focusing on steganalysis for neural networks based on the distribution of network output, compared to conventional digital media such as images, audio, and video. Overall, the proposed scheme offers a promising approach to enhancing the security of deep neural networks against data hiding attacks.
Funder
National Natural Science Foundation of China
Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission
Subject
Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)
Reference48 articles.
1. Shehab, D.A., and Alhaddad, M.J. (2022). Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research. Symmetry, 14.
2. Hiding data in images by simple LSB substitution;Chan;Pattern Recognit.,2004
3. LSB matching revisited;Mielikainen;IEEE Signal Process. Lett.,2006
4. Pevný, T., Filler, T., and Bas, P. (2010). Proceedings of the International Workshop on Information Hiding, Springer.
5. Holub, V., and Fridrich, J. (2012, January 2–5). Designing steganographic distortion using directional filters. Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security (WIFS), Costa Adeje, Spain.