Abstract
Traditional steganography embeds confidential information by modifying the carrier at the symbol level, e.g., the pixels of an image or the words of a text. Since modification traces will inevitably be left on the carrier, it is hard to resist the detection of the steganalysis algorithms. To address this problem, this paper proposes a novel steganographic framework called multi-modal steganography, which hides secret messages at the semantic level. In this framework, multi-modal covers are projected into a common semantic space, in which their relevancy can be measured. The confidential information can be embedded in the semantic relevancy among the covers with a relevancy-message mapping algorithm. By choosing and sending a series of original multi-modal covers, the secret messages are transmitted to the receiver. In this paper, we adopt text and image as the two modalities. A visual semantic embedding model is utilized to measure the relevancy between the texts and images. Both the theoretical analysis and experiments demonstrate that the proposed multi-modal steganography has good resistance to the existing steganalysis methods and high quality of concealment.
This research is supported by the National Key R&D Program (2018YFB0804103) and the National Natural Science Foundation of China (No. U1705261 and No. U1836204).
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References
Du, Y., Yin, Z., Zhang, X.: Improved lossless data hiding for JPEG images based on histogram modification. Comput. Mater. Continua 55(3), 495–507 (2018)
Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: VSE++: improving visual-semantic embeddings with hard negatives. arXiv preprint arXiv:1707.05612 (2017)
Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, Cambridge (2009)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Hu, Y., Li, H., Song, J., Huang, Y.: MM-Stega: multi-modal steganography based on text-image matching. In: Sun, X., Wang, J., Bertino, E. (eds.) ICAIS 2020. CCIS, vol. 1254, pp. 313–325. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-8101-4_29
Hu, Y., Zheng, L., Yang, Y., Huang, Y.: Twitter100k: a real-world dataset for weakly supervised cross-media retrieval. IEEE Trans. Multimed. 20(4), 927–938 (2018)
Huang, Y., Liu, C., Tang, S., Bai, S.: Steganography integration into a low-bit rate speech codec. IEEE Trans. Inf. Forensics Secur. 7(6), 1865–1875 (2012)
Li, L., Zhang, W., Chen, K., Zha, H., Yu, N.: Side channel steganalysis: when behavior is considered in steganographer detection. Multimed. Tools Appl. 78(7), 8041–8055 (2019)
Li, X., Yang, B., Cheng, D., Zeng, T.: A generalization of LSB matching. IEEE Sig. Process. Lett. 16(2), 69–72 (2009)
Lie, W.N., Lin, G.S.: A feature-based classification technique for blind image steganalysis. IEEE Trans. Multimed. 7(6), 1007–1020 (2005)
Westfeld, A.: F5—A steganographic algorithm. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 289–302. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45496-9_21
Wu, S., Zhong, S., Liu, Y.: Deep residual learning for image steganalysis. Multimed. Tools Appl. 77(9), 10437–10453 (2018)
Xu, G., Wu, H., Shi, Y.: Structural design of convolutional neural networks for steganalysis. IEEE Sig. Process. Lett. 23(5), 708–712 (2016)
Yang, Z., Guo, X., Chen, Z., Huang, Y., Zhang, Y.: RNN-Stega: linguistic steganography based on recurrent neural networks. IEEE Trans. Inf. Forensics Secur. 14(5), 1280–1295 (2018)
Yang, Z., Hu, Y., Huang, Y., Zhang, Y.: Behavioral security in covert communication systems. arXiv preprint arXiv:1910.09759 (2019)
Yang, Z., Huang, Y., Zhang, Y.: A fast and efficient text steganalysis method. IEEE Sig. Process. Lett. 26(4), 627–631 (2019)
Yang, Z., Wang, K., Li, J., Huang, Y., Zhang, Y.: TS-RNN: text steganalysis based on recurrent neural networks. IEEE Sig. Process. Lett. 26(12), 1743–1747 (2019). https://doi.org/10.1109/LSP.2019.2920452
Yang, Z., Zhang, S., Hu, Y., Hu, Z., Huang, Y.: VAE-Stega: linguistic steganography based on variational auto-encoder. IEEE Trans. Inf. Forensics Secur. 16, 880–895 (2020)
Zhang, X., Peng, F., Long, M.: Robust coverless image steganography based on DCT and LDA topic classification. IEEE Trans. Multimed. 20(12), 3223–3238 (2018)
Zhang, Y., Ye, D., Gan, J., Li, Z., Cheng, Q.: An image steganography algorithm based on quantization index modulation resisting scaling attacks and statistical detection. Comput. Mater. Continua 56(1), 151–167 (2018)
Zheng, S., Wang, L., Ling, B., Hu, D.: Coverless information hiding based on robust image hashing. In: Huang, D.-S., Hussain, A., Han, K., Gromiha, M.M. (eds.) ICIC 2017. LNCS (LNAI), vol. 10363, pp. 536–547. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63315-2_47
Zhou, Z., Mu, Y., Wu, Q.J.: Coverless image steganography using partial-duplicate image retrieval. Soft Comput. 23(13), 4927–4938 (2019). https://doi.org/10.1007/s00500-018-3151-8
Zhou, Z., Sun, H., Harit, R., Chen, X., Sun, X.: Coverless image steganography without embedding. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds.) ICCCS 2015. LNCS, vol. 9483, pp. 123–132. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27051-7_11
Zhou, Z., Wu, Q.J., Yang, C.N., Sun, X., Pan, Z.: Coverless image steganography using histograms of oriented gradients-based hashing algorithm. J. Internet Technol. 18(5), 1177–1184 (2017)
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Hu, Y., Yang, Z., Cao, H., Huang, Y. (2021). Multi-modal Steganography Based on Semantic Relevancy. In: Zhao, X., Shi, YQ., Piva, A., Kim, H.J. (eds) Digital Forensics and Watermarking. IWDW 2020. Lecture Notes in Computer Science(), vol 12617. Springer, Cham. https://doi.org/10.1007/978-3-030-69449-4_1
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