Abstract
In general, image deformations caused by different steganographic algorithms are extremely small and of high similarity. Therefore, detecting and identifying multiple steganographic algorithms are not easy. Although recent steganalytic methods using deep learning showed highly improved detection accuracy, they were dedicated to binary classification, i.e., classifying between cover images and their stego images generated by a specific steganographic algorithm. In this paper, we aim at achieving quinary classification, i.e., detecting (=classifying between stego and cover images) and identifying four spatial steganographic algorithms (LSB, PVD, WOW, and S-UNIWARD), and propose to use a hierarchical structure of convolutional neural networks (CNN) and residual neural networks (ResNet). Experimental results show that the proposed method can improve the classification accuracy by 17.71% compared to the method that uses a single CNN.
Keywords
- Steganalysis
- Image steganography
- Convolutional neural network
- Residual neural network
- Hierarchical structure
- Quinary classification
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C.K. Chan, L.M. Cheng, Hiding data in images by simple LSB substitution. Pattern Recogn. 37, 469–474 (2004)
D. Wu, W. Tsai, A steganographic method for images by pixel-value differencing. Pattern Recogn. 24, 1613–1626 (2003)
T. Pevný, T. Filler, P. Bas, Using high-dimensional image models to perform highly undetectable steganography. LNCS, Vol. 6387 (Springer, Berlin, Heidelberg, 2010)
V. Holub, J. Fridrich, Designing steganographic distortion using directional filters, in Proceedings of IEEE International Workshop on Information Forensics and Security, pp. 234–239 (2012)
V. Holub, J. Fridrich, T. Denemark, Universal distortion function for steganography in an arbitrary domain. EURASIP J. Info. Security 1 (2014)
S. Manoharan, An empirical analysis of RS steganalysis, in Proceedings of Third International Conference on Internet Monitoring and Protection, pp. 172–177 (2008)
Y. Hou, R. Ni, Y. Zhao, Steganalysis to Adaptive pixel pair matching using two-group subtraction pixel adjacency model of covers, in Proceedings of International Conference on Signal Processing, pp. 1864–1867 (2014)
J. Fridrich, J. Kodovsky, Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7, 868–882 (2012)
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with DEEP convolutional neural networks. Commun. ACM 60, 84–90 (2017)
G. Xu, H.-Z. Wu, Y.Q. Shi, Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23, 708–712 (2016)
Y. Yuan, Z. Wei, B. Feng, J. Weng, Steganalysis with CNN using multi-channels filtered residuals, in Proceedings of International Conference on Cloud Computing and Security, pp. 110–120 (2017)
B. Li, W. Wei, A. Ferreira, S. Tan, ReST-Net: diverse activation modules and parallel subnets-based CNN for spatial image steganalysis. IEEE Signal Process. Lett. 25, 650–654 (2018)
X. Deng, B. Chen, W. Luo, D. Luo, Fast and Effective global covariance pooling network for image steganalysis, in Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, pp. 230–234 (2019)
W. Songtao, S. Zhong, Y. Liu, Deep residual learning for image steganalysis. Multim. Tools Appl. 77, 10437–10453 (2018)
S. Kang, H. Park, J.-I. Park, CNN-based ternary classification for image steganalysis. Electronics 8, 1225 (2019)
P.Bas, T. Filler, T. Pevný, “Break Our Steganographic System”: the Ins and outs of organizing BOSS. LNCS, Vol. 6958, pp. 59–70 (Springer, Berlin, Heidelberg, 2011)
Acknowledgements
This work was supported by the research fund of the Signal Intelligence Research Center supervised by the Defense Acquisition Program Administration and Agency for the Defense Development of Korea.
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Kang, S., Park, H., Park, JI. (2021). Classification of Multiple Steganographic Algorithms Using Hierarchical CNNs and ResNets. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A., Kumar, P. (eds) Proceedings of International Conference on Smart Computing and Cyber Security. SMARTCYBER 2020. Lecture Notes in Networks and Systems, vol 149. Springer, Singapore. https://doi.org/10.1007/978-981-15-7990-5_36
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DOI: https://doi.org/10.1007/978-981-15-7990-5_36
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