Abstract
With the rapid development of deep learning, a lot of CNN-based steganalyzers have emerged. This kind of steganalyzer uses statistical learning to investigate the properties caused by steganography, which is the most efficient approaches for breaking information hiding. However, we find a vulnerability of CNN-based steganalyzer that it can be defeated by dual operations. In this paper, we propose an easy yet effective algorithm to perturb the stego images against neural network, which can evade CNN-based steganalyzer with high probabilities. We elaborated on the theoretical basis of the method we proposed and proved the feasibility of this method through experiments.
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References
Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications [M]. Cambridge University Press, New York (2009)
Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3), 920–935 (2011)
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: Proceedings of IEEE International Workshop on Information Forensics and Security, Binghamton, NY, USA, pp. 234–239 (2012)
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1–13 (2014)
Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: Proceedings of the IEEE International Conference on Image Processing, Paris, France, pp. 4206–4210 (2014)
Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inf. Forensics Secur. 11(2), 221–234 (2016)
Fridrich, J., Kodovský, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Qian, Y., Dong, J., Wang, W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. Proc. SPIE 9409, 94090J (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Xu, G., Wu, H.-Z., Shi, Y.Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett. 23(5), 708–712 (2016)
Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., Fergus, R.: Intriguing properties of neural networks. ICLR (2014). arXiv:1312.6199
Barreno, M., Nelson, B., Sears, R., Joseph, A.D., Tygar, J.D.: Can machine learning be secure? In: Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, pp. 16–25 (2006). ACM
Barreno, M., Nelson, B., Joseph, A.D., Tygar, J.: The security of machine learning. Mach. Learn. 81(2), 121–148 (2010)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2014). arXiv:1412.6572
Su, J., Vargas, D.V., Kouichi, S.: One pixel attack for fooling deep neural networks (2017)
Bas, P., Filler, T., Pevný, T.: Break our steganographic system: the ins and outs of organizing BOSS. In: Proceedings of the 13th International Conference on Information Hiding, Prague, Czech Republic, pp. 59–70 (2011)
Acknowledgements
This work was supported by the Natural Science Foundation of China (U1736213, U1536108, 61572308, 61103181, U1636206, 61373151, and 61525203), the Natural Science Foundation of Shanghai (18ZR1427500), the Shanghai Dawn Scholar Plan (14SG36) and the Shanghai Excellent Academic Leader Plan (16XD1401200).
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Qian, Z., Huang, C., Wang, Z., Zhang, X. (2019). Breaking CNN-Based Steganalysis. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_50
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DOI: https://doi.org/10.1007/978-981-13-5841-8_50
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