Abstract
The topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected layers on the effectiveness of classification was conducted. Based on the conclusions from the studies, an improved convolutional neural network was created, which is characterized by the state-of-art level of classification efficiency but containing 20 times less parameters to learn during the training process. Smaller number of learnable parameters results in faster network learning, easier convergence, and smaller memory and computing power requirements. The paper contains description of the current state of art, description of the experimental environment, structures of the studied networks and the results of classification accuracy.
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References
Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, Cambridge (2010). ISBN 978-0-521-19019-0. https://doi.org/10.1109/msp.2011.941841
Czaplewski, B.: Current trends in the field of steganalysis and guidelines for constructions of new steganalysis schemes. Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne [= Telecommun. Rev. + Telecommun. News] 10, 1121–1125 (2017). https://doi.org/10.15199/59.2017.10.3
Fridrich, J., Kodovský, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Security 7(3), 868–882 (2012). https://doi.org/10.1109/TIFS.2012.2190402
He, F., Zhong, S., Chen, K.: An effective ensemble-based classification algorithm for high-dimensional steganalysis. J. Softw. 9(7), 1833–1840 (2014). https://doi.org/10.4304/jsw.9.7.1833-1840
Pibre, L., Pasquet, J., Ienco, D., Chaumont, M.: Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch. In: Proceedings of Media Watermarking, Security, and Forensics, MWSF 2016, Part of I&ST International Symposium on Electronic Imaging, EI 2016, San Francisco, California, USA, pp. 1–11 (2016). https://doi.org/10.2352/ISSN.2470-1173.2016.8.MWSF-078
Tan, S., Li, B.: Stacked convolutional auto-encoders for steganalysis of digital images. In: Proceedings of Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014, Siem Reap, Cambodia, pp. 1–4 (2014). https://doi.org/10.1109/apsipa.2014.7041565
Qian, Y., Dong, J., Wang, W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Proceedings of Media Watermarking, Security, and Forensics 2015, MWSF 2015, Part of IS&T/SPIE Annual Symposium on Electronic Imaging, SPIE 2015, San Francisco, California, USA, vol. 9409, pp. 94090J–94090J–10 (2015). https://doi.org/10.1117/12.2083479
Xu, G., Wu, H.Z., Shi, Y.Q.: Ensemble of CNNs for steganalysis: an empirical study. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, Vigo, Galicia, Spain, IH&MMSec 2016, pp. 103–107 (2016). https://doi.org/10.1145/2909827.2930798
Zeng, J., Tan, S., Li, B., Huang, J.: Pre-training via fitting deep neural network to rich-model features extraction procedure and its effect on deep learning for steganalysis. In: Proceedings of the Media Watermarking, Security, and Forensics 2017, MWSF 2017, Part of IS&T Symposium on Electronic Imaging, EI 2017, Burlingame, California, USA, p. 6 (2017). https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-324
Chen, M., Sedighi, V., Boroumand, M., Fridrich, S.: JPEG-phase-aware convolutional neural network for steganalysis of JPEG images. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, Drexel University in Philadelphia, PA, IH&MMSec 2017, pp. 75–84 (2017). https://doi.org/10.1145/3082031.3083248
Xu, G.: Deep convolutional neural network to detect JUNIWARD. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, Drexel University in Philadelphia, PA, IH&MMSec 2017, pp. 67–73 (2017). https://doi.org/10.1145/3082031.3083236
Yedroudj, M., Comby, F., Chaumont, M.: Yedroudj-Net: an efficient CNN for spatial steganalysis. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, Alberta, Canada (2018). https://doi.org/10.1109/icassp.2018.8461438
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Czaplewski, B. (2019). An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_7
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DOI: https://doi.org/10.1007/978-3-030-30508-6_7
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