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Active Defense Against Image Steganography

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Artificial Intelligence Security and Privacy (AIS&P 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14509))

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Abstract

Image steganalysis is the art and science of detecting whether an image contains secrete messages or not, which can prevent malicious usage of steganography. However, steganalysis belongs to passive defense, in the sense that it can only be applied after the stego image is generated. Therefore, there still exists loophole that secrete messages communication could already been accomplished when the stego image is detected. To eliminate malicious steganography from the source, in this paper, an active defensive framework for deep image steganography called ADPI (Active Defense based on Perturbation Injection) is proposed, wherein a defender competes against a steganographer to learn the active defensive strategy. Specifically, on the side of the defender, a generator is adopted to take the original cover image as input, and learn the imperceptible perturbation map. Such perturbation map is added with the original cover image as the enhanced cover image. On the side of the steganographer, a steganographic network is applied to perform message embedding and extraction on the enhanced cover image. The key of ADPI is that the perturbation map is optimized with the goal of reducing the accuracy of the message recovery while maintaining its invisibility. By this means, the active defense can be launched in an effective and imperceptible manner. Experimental results show that the proposed ADPI can be applied to defend against various steganographic methods.

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References

  1. Holub, V., Fridrich, J.J.: Designing steganographic distortion using directional filters. In: Proceedings of the IEEE International Workshop on Information Forensics and Security, pp. 234–239 (2012)

    Google Scholar 

  2. Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Proceedings of the 12th Information Hiding Workshop, pp. 161–177 (2010)

    Google Scholar 

  3. 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, pp. 4026–4210 (2014)

    Google Scholar 

  4. Holub, V., Fridrich, J.J.: Digital image steganography using universal distortion. In: Proceedings of the 1st ACM Information Hiding and Multimedia Security Workshop, pp. 59–68 (2013)

    Google Scholar 

  5. Hayes, J., Danezis, G.: Generating steganographic images via adversarial training. In: Proceedings of the 30th Conference on Neural Information Processing Systems, pp. 1954–1963 (2017)

    Google Scholar 

  6. Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  7. Zhu, J., Kaplan, R., Johnson, J., Fei-Fei, L.: HiDDeN: hiding data with deep networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 682–697. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_40

    Chapter  Google Scholar 

  8. Zhang, K.A., Cuesta-Infante, A., Xu, L., Veeramachaneni, K.: Steganogan: high capacity image steganography with GANs. arXiv preprint arXiv:1901.03892 (2019)

  9. Tan, J., Liao, X., Liu, J., Cao, Y., Jiang, H.: Channel attention image steganography with generative adversarial networks. IEEE Trans. Network Sci. Eng. 9(2), 888–903 (2021)

    Article  Google Scholar 

  10. Lu, S.P., Wang, R., Zhong, T., Rosin, P.L.: Large-capacity image steganography based on invertible neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10816–10825 (2021)

    Google Scholar 

  11. Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2011)

    Article  Google Scholar 

  12. Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)

    Article  Google Scholar 

  13. Li, B., Li, Z., Zhou, S., Tan, S., Zhang, X.: New steganalytic features for spatial image steganography based on derivative filters and threshold LBP operator. IEEE Trans. Inf. Forensics Secur. 13(5), 1242–1257 (2017)

    Article  Google Scholar 

  14. Song, X., Liu, F., Yang, C., Luo, X., Zhang, Y.: Steganalysis of adaptive jpeg steganography using 2D gabor filters. In: Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, pp. 15–23 (2015)

    Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Zhang, R., Zhu, F., Liu, J., Liu, G.: Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis. IEEE Trans. Inf. Forensics Secur. 15, 1138–1150 (2019)

    Article  Google Scholar 

  17. You, W., Zhang, H., Zhao, X.: A SIAMESE CNN for image steganalysis. IEEE Trans. Inf. Forensics Secur. 16, 291–306 (2021)

    Article  Google Scholar 

  18. Deng, X., Chen, B., Luo, W., Luo, D.: 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)

    Google Scholar 

  19. Weng, S., Chen, M., Yu, L., Sun, S.: Lightweight and effective deep image steganalysis network. IEEE Signal Process. Lett. 29, 1888–1892 (2022)

    Article  Google Scholar 

  20. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  21. Zhang, C., Benz, P., Karjauv, A., Sun, G., Kweon, I.S.: UDH: universal deep hiding for steganography, watermarking, and light field messaging. In: Proceedings of the 33rd Conference on Neural Information Processing Systems, pp. 10223–10234 (2020)

    Google Scholar 

  22. Almohammad, A., Ghinea, G.: Stego image quality and the reliability of PSNR. In: Proceedings of the 2nd International Conference on Image Processing Theory, Tools and Applications, pp. 215–220. IEEE (2010)

    Google Scholar 

  23. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

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Acknowledgements

Weixuan Tang is the corresponding author. This work was supported by NSFC (Grant 62002075), Guangdong Basic and Applied Basic Research Foundation (Grant 2023A1515011428), the Science and Technology Foundation of Guangzhou (Grant 2023A04J1723).

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Authors and Affiliations

  1. Institute of Artificial Intelligence, Guangzhou University, Guangzhou, China

    Weixuan Tang & Yadong Liu

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  1. Weixuan Tang
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  2. Yadong Liu
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Correspondence to Weixuan Tang .

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Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Tampere University, Tampere, Finland

    Moncef Gabbouj

  3. Guangzhou University, Guangzhou, China

    Jin Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Tang, W., Liu, Y. (2024). Active Defense Against Image Steganography. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_10

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  • DOI: https://doi.org/10.1007/978-981-99-9785-5_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9784-8

  • Online ISBN: 978-981-99-9785-5

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