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Improving Audio Steganalysis Using Deep Residual Networks

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Digital Forensics and Watermarking (IWDW 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12022))

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Abstract

In this paper, we propose an effective audio steganalysis scheme based on deep residual convolutional networks in the temporal domain. Firstly, considering the weak difference between cover and stego, a high pass filter is adopted in the proposed network which is used to calculate the residual map of the audio signal. Then, comparing with convolutional neural networks (CNNs) based audio steganalysis in recent studies, the deeper network structure and complicated convolutional modules are considered to capture the complex statistical characteristic of steganography. Finally, batch normalization layers and shortcut connections are applied to decrease the dangers of over-fitting and accelerate the convergence of back-propagation. In the experiments, we compared the proposed scheme with CNNs based and hand-crafted features based audio steganalysis methods to detect the various steganographic algorithms on speech and music audio clips respectively. The experimental results demonstrate that the proposed scheme is able to detect multiple state-of-the-art audio steganographic schemes with different payloads effectively and outperforms several recently proposed audio steganalysis methods.

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References

  1. Boroumand, M., Chen, M., Fridrich, J.: Deep residual network for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 14(5), 1181–1193 (2018)

    Article  Google Scholar 

  2. Chen, B., Luo, W., Li, H.: Audio steganalysis with convolutional neural network. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 85–90. ACM (2017)

    Google Scholar 

  3. Eger, S., Youssef, P., Gurevych, I.: Is it time to swish, comparing deep learning activation functions across NLP tasks. arXiv preprint arXiv:1901.02671 (2019)

  4. Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  5. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

    Google Scholar 

  6. Han, C., Xue, R., Zhang, R., Wang, X.: A new audio steganalysis method based on linear prediction. Multimedia Tools Appl. 77(12), 15431–15455 (2017). https://doi.org/10.1007/s11042-017-5123-x

    Article  Google Scholar 

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)

    Google Scholar 

  8. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part IV. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  9. Jin, C., Wang, R., Yan, D.: Steganalysis of MP3Stego with low embedding-rate using Markov feature. Multimed. Tools Appl. 76(5), 6143–6158 (2016). https://doi.org/10.1007/s11042-016-3264-y

    Article  Google Scholar 

  10. Ker, A.D.: The square root law of steganography: Bringing theory closer to practice. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 33–44. ACM (2017)

    Google Scholar 

  11. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  12. Kraetzer, C., Dittmann, J.: Mel-cepstrum-based steganalysis for VoIP steganography. In: Proceedings of SPIE conference on the Security, Steganography and Watermarking of Multimedia. pp. 5–12. SPIE (2007)

    Google Scholar 

  13. Lin, Y., Wang, R., Yan, D., Dong, L., Zhang, X.: Audio steganalysis with improved convolutional neural network. In: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, pp. 210–215. ACM (2019)

    Google Scholar 

  14. Liu, Q., Sung, A.H., Qiao, M.: Temporal derivative-based spectrum and Mel-cepstrum audio steganalysis. IEEE Trans. Inf. Forensics Secur. 4(3), 359–368 (2009)

    Article  Google Scholar 

  15. Liu, Q., Sung, A.H., Qiao, M.: Derivative-based audio steganalysis. ACM Trans. Multimed. Comput. Commun. Appl. 7(3), 1–19 (2011)

    Article  Google Scholar 

  16. Luo, W., Zhang, Y., Li, H.: Adaptive audio steganography based on advanced audio coding and syndrome-trellis coding. In: Kraetzer, C., Shi, Y.-Q., Dittmann, J., Kim, H.J. (eds.) IWDW 2017. LNCS, vol. 10431, pp. 177–186. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64185-0_14

    Chapter  Google Scholar 

  17. Ren, Y., Xiong, Q., Wang, L.: A steganalysis scheme for AAC audio based on MDCT difference between intra and inter frame. In: Kraetzer, C., Shi, Y.-Q., Dittmann, J., Kim, H.J. (eds.) IWDW 2017. LNCS, vol. 10431, pp. 217–231. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64185-0_17

    Chapter  Google Scholar 

  18. Shi, X., Li, B., Tan, S.: Preprocessing layer in spatial steganalysis based on deep learning. J. Appl. Sci. 36(2), 309–320 (2018)

    Google Scholar 

  19. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  20. Sun, S., Chen, W., Wang, L., Liu, X., Liu, T.Y.: On the depth of deep neural networks: a theoretical view. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2066–2072 (2016)

    Google Scholar 

  21. Wang, Y., Yang, K., Yang, Y., Zhang, Z., Yi, X., Zhao, X.: Audio steganalysis dataset (2019). https://ieee-dataport.org/documents/audio-steganalysis-dataset

  22. Wang, Y., Yang, K., Yi, X., Zhao, X., Xu, Z.: CNN-based steganalysis of MP3 Steganography in the entropy code domain. In: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 55–65. ACM (2018)

    Google Scholar 

  23. Wu, S., Zhong, S.H., Liu, Y.: Steganalysis via deep residual network. In: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems, pp. 1233–1236. IEEE (2016)

    Google Scholar 

  24. Wu, S., Zhong, S., Liu, Y.: Deep residual learning for image steganalysis. Multimed. Tools Appl. 77(9), 10437–10453 (2017). https://doi.org/10.1007/s11042-017-4440-4

    Article  Google Scholar 

  25. Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)

    Article  Google Scholar 

  26. Zou, M., Li, Z.: A wav-audio steganography algorithm based on amplitude modifying. In: Tenth International Conference on Computational Intelligence and Security, pp. 489–493. IEEE (2014)

    Google Scholar 

Download references

Acknowledgments

This work was supported by NSFC under U1736214, 61902391 and 61972390, and National Key Technology R&D Program under 2019QY0700 and 2016QY15Z2500.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Zhenyu Zhang, Xiaowei Yi & Xianfeng Zhao

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100093, China

    Zhenyu Zhang, Xiaowei Yi & Xianfeng Zhao

Authors
  1. Zhenyu Zhang
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  2. Xiaowei Yi
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  3. Xianfeng Zhao
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Corresponding author

Correspondence to Xianfeng Zhao .

Editor information

Editors and Affiliations

  1. College of Cybersecurity, Sichuan University, Chengdu, China

    Hongxia Wang

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xianfeng Zhao

  3. Department of ECE, New Jersey Institute of Technology, Newark, NJ, USA

    Yunqing Shi

  4. Graduate School of Information Study, Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

  5. Department of Information Engineering, University of Florence, Florence, Italy

    Alessandro Piva

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Zhang, Z., Yi, X., Zhao, X. (2020). Improving Audio Steganalysis Using Deep Residual Networks. In: Wang, H., Zhao, X., Shi, Y., Kim, H., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2019. Lecture Notes in Computer Science(), vol 12022. Springer, Cham. https://doi.org/10.1007/978-3-030-43575-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-43575-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43574-5

  • Online ISBN: 978-3-030-43575-2

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