数字图像隐写分析综述

doi:10.3969/j.issn.0255-8297.2024.05.001

摘要/Abstract

摘要： 数字隐写是机密信息安全传递的重要方式，将机密信息隐藏于普通多媒体数据（图像或视音频）中可以实现隐蔽传输。而发现机密信息的隐蔽传输可采用隐写分析技术，隐写分析根据隐写引起的载体数据统计异常来判断多媒体数据是否含有秘密信息。近年来，隐写与隐写分析在相互对抗中不断进步与发展。随着社交网络的兴起，数字图像已成为社交媒介之一并广泛传播。本文以数字图像为例，梳理了近十余年数字图像隐写分析研究的发展现状；综述了传统隐写分析与深度学习隐写分析；探讨了各类方法面临的挑战，并展望了隐写分析的发展趋势。

关键词: 隐写, 隐写分析, 数字图像, 特征提取, 深度学习

Abstract: Digital steganography plays a crucial role in securely transmitting confidential data by concealing it within common multimedia, such as images, videos, and audio, to facilitate covert communication. To discover the covert communication of steganography, the technique of steganalysis can be employed. Steganalysis determines whether a given multimedia object contains secret data according to the statistical anomaly of stego data caused by steganography. In recent years, both steganography and steganalysis have made significant progress and development in their mutual confrontation, particularly in the context of the growing prevalence of digital images on social networks. Focusing on digital images, this paper sorts out the development of steganalysis in the past decade, and reviews the traditional steganalysis and deep learning based-steganalysis. Then, the limitations of each approach are discussed. Finally, the study offers insights into the prospective development trends in steganalysis.

Key words: steganography, steganalysis, digital images, feature extraction, deep learning

中图分类号:

TP309

王子驰, 李斌, 冯国瑞, 张新鹏. 数字图像隐写分析综述[J]. 应用科学学报, 2024, 42(5): 723-732.

WANG Zichi, LI Bin, FENG Guorui, ZHANG Xinpeng. Review of Steganalysis for Digital Images[J]. Journal of Applied Sciences, 2024, 42(5): 723-732.

参考文献

[1] Filler T, Judas J, Fridrich J. Minimizing additive distortion in steganography using syndrome-trellis codes [J]. IEEE Transactions on Information Forensics and Security, 2011, 6(3): 920-935.
[2] Holub V, Fridrich J. Digital image steganography using universal distortion [C]//1st ACM Workshop on Information Hiding and Multimedia Security, 2013: 59-68.
[3] Li B, Wang M, Huang J W, et al. A new cost function for spatial image steganography [C]//2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014: 4206-4210.
[4] Li B, Wang M, Li X L, et al. A strategy of clustering modification directions in spatial image steganography [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(9): 1905-1917.
[5] Denemark T, Fridrich J. Steganography with multiple JPEG images of the same scene [J]. IEEE Transactions on Information Forensics and Security, 2017, 12(10): 2308-2319.
[6] Wang Z C, Qian Z X, Zhang X P, et al. On improving distortion functions for JPEG steganography [J]. IEEE Access, 2018, 6: 74917-74930.
[7] Tang W X, Li B, Tan S Q, et al. CNN-based adversarial embedding for image steganography [J]. IEEE Transactions on Information Forensics and Security, 2019, 14(8): 2074-2087.
[8] Bernard S, Bas P, Klein J, et al. Explicit optimization of min max steganographic game [J]. IEEE Transactions on Information Forensics and Security, 2020, 16: 812-823.
[9] Zhang Y, Luo X Y, Guo Y Q, et al. Multiple robustness enhancements for image adaptive steganography in lossy channels [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(8): 2750-2764.
[10] Tao J Y, Li S, Zhang X P, et al. Towards robust image steganography [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(2): 594-600.
[11] Li S, Zhang X P. Toward construction-based data hiding: from secrets to fingerprint images [J]. IEEE Transactions on Image Processing, 2019, 28(3): 1482-1497.
[12] Zhang X, Peng F, Long M. Robust coverless image steganography based on DCT and LDA topic classification [J]. IEEE Transactions on Multimedia, 2018, 20(12): 3223-3238.
[13] Hu D H, Wang L, Jiang W J, et al. A novel image steganography method via deep convolutional generative adversarial networks [J]. IEEE Access, 2018, 6: 38303-38314.
[14] Fridrich J, Kodovsky J. Rich models for steganalysis of digital images [J]. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 868-882.
[15] Holub V, Fridrich J. Low-complexity features for JPEG steganalysis using undecimated DCT [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(2): 219-228.
[16] Boroumand M, Fridrich J. Applications of explicit non-linear feature maps in steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2018, 13(4): 823-833.
[17] Denemark T D, Boroumand M, Fridrich J. Steganalysis features for content-adaptive JPEG steganography [J]. IEEE Transactions on Information Forensics and Security, 2016, 11(8): 1736-1746.
[18] Kodovsky J, Fridrich J, Holub V. Ensemble classifiers for steganalysis of digital media [J]. IEEE Transactions on Information Forensics and Security, 2012, 7(2): 432-444.
[19] Ker A D, Pevný T. A new paradigm for steganalysis via clustering [C]//Media Watermarking, Security, and Forensics III. SPIE, 2011, 7880: 312-324.
[20] Ker A D, Pevný T. The steganographer is the outlier: realistic large-scale steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2014, 9(9): 1424-1435.
[21] Li F Y, Wu K, Lei J S, et al. Steganalysis over large-scale social networks with high-order joint features and clustering ensembles [J]. IEEE Transactions on Information Forensics and Security, 2016, 11(2): 344-357.
[22] Xu G S, Wu H Z, Shi Y Q. Structural design of convolutional neural networks for steganalysis [J]. IEEE Signal Processing Letters, 2016, 23(5): 708-712.
[23] Ye J, Ni J Q, Yi Y. Deep learning hierarchical representations for image steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2017, 12(11): 2545-2557.
[24] Zeng J S, Tan S Q, Li B, et al. Large-scale JPEG image steganalysis using hybrid deeplearning framework [J]. IEEE Transactions on Information Forensics and Security, 2018, 13(5): 1200-1214.
[25] Li B, Wei W H, Ferreira A, et al. ResT-Net: diverse activation modules and parallel subnets-based CNN for spatial image steganalysis [J]. IEEE Signal Processing Letters, 2018, 25(5): 650-654.
[26] You W K, Zhang H, Zhao X F. A siamese CNN for image steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 291-306.
[27] Zeng J S, Tan S Q, Liu G Q, et al. WISERNet: wider separate-then-Reunion network for steganalysis of color images [J]. IEEE Transactions on Information Forensics and Security, 2019, 14(10): 2735-2748.
[28] Chen M, Boroumand M, Fridrich J. Reference channels for steganalysis of images with convolutional neural networks [C]//ACM Workshop on Information Hiding and Multimedia Security, 2019: 188-197.
[29] Zhang R, Zhu F, Liu J Y, et al. Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 1138-1150.
[30] Wu S T, Zhong S H, Liu Y. A novel convolutional neural network for image steganalysis with shared normalization [J]. IEEE Transactions on Multimedia, 2020, 22(1): 256-270.
[31] Boroumand M, Chen M, Fridrich J. Deep residual network for steganalysis of digital images [J]. IEEE Transactions on Information Forensics and Security, 2019, 14(5): 1181-1193.
[32] Ni D N, Feng G R, Shen L Q, et al. Selective ensemble classification of image steganalysis via deep Q network [J]. IEEE Signal Processing Letters, 2019, 26(7): 1065-1069.
[33] Singh B, Sur A, Mitra P. Steganalysis of digital images using deep fractal network [J]. IEEE Transactions on Computational Social Systems, 2021, 8(3): 599-606.
[34] Jia J, Luo M, Liu J S, et al. Multiperspective progressive structure adaptation for JPEG steganography detection across domains [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(8): 3660-3674.
[35] Su A, Zhao X. Arbitrary-sized JPEG steganalysis based on fully convolutional network [C]//Digital Forensics and Watermarking: 20th International Workshop, Revised Selected Papers. Springer, 2021: 197.
[36] Jia J, Zhai L M, Ren W X, et al. Transferable heterogeneous feature subspace learning for JPEG mismatched steganalysis [J]. Pattern Recognition, 2020, 100: 107105.
[37] Yang L R, Men M, Xue Y M, et al. Transfer subspace learning based on structure preservation for JPEG image mismatched steganalysis [J]. Signal Processing: Image Communication, 2021, 90: 116052.
[38] Qian Y L, Dong J, Wang W, et al. Learning representations for steganalysis from regularized CNN model with auxiliary tasks [C]//2015 International Conference on Communications, Signal Processing, and Systems. Springer, 2016: 629-637.
[39] Ozcan S, Mustacoglu A F. Transfer learning effects on image steganalysis with pre-trained deep residual neural network model [C]//2018 IEEE International Conference on Big Data. IEEE, 2018: 2280-2287.
[40] Feng C Y, Kong X W, Li M, et al. Contribution-based feature transfer for JPEG mismatched steganalysis [C]//2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017: 500-504.