In image forensics, steganography and steganalysis are like the two ends of the same coin. image steganography is a technique to conceal secret messages in the images by slightly modifying the pixel values. Corresponding to image steganography, steganalysis is the process to reveal the presence of the hidden message in images. Recently, steganalysis are focusing on defending content-adaptive steganographic schemes, for example WOW, HILL and S-UNIWARD, etc. Fig.1 [1] illustrates the modifications of cover image from different steganographic method. Content-adaptive steganography is lean to do modifications on complex texture regions, which makes embedding traces less detectable for steganalyzers.
Traditionally, hand crafted features together with machine learning classifiers have good performance on steganalysis, such as Spatial Rich Model and its variants. After the emerging of neural networks, different CNN architectures are utilized in the steganalysis literature. Because of the important property that CNNs are able to extract complex statistical dependencies from high dimensional input and learn hierarchical representations, CNN-based features usually achieve better performance than traditional hand-crafted features. However, CNN based models are suffering from long training time, large model size and enormous consumption of computation resources.
We would like to utilize green learning methodology in steganalysis field, by incorporating Saab transform as feature extraction module in the future. Saab transform has shown its capability of extracting the high frequency representations in a feedforward way and preserving the light-weighted model size at the same time.
References:
[1] Tang, Weixuan, et al. “Adaptive steganalysis based on embedding probabilities of pixels.” IEEE Transactions on Information Forensics and Security 11.4 (2015): 734-745.
[2] C.-C. J. Kuo and Y. Chen, “On data-driven saak transform,” Journal of Visual Communication and Image Representation, vol. 50, pp. 237–246, 2018.
[3] C.-C. J. Kuo, M. Zhang, S. Li, J. Duan, and Y. Chen, “Interpretable convolutional neural networks via feedforward design,” Journal of Visual Communication and Image Representation, 2019.
