Journal of Applied Sciences ›› 2024, Vol. 42 ›› Issue (5): 723-732.doi: 10.3969/j.issn.0255-8297.2024.05.001
• Signal and Information Processing • Previous Articles
WANG Zichi1, LI Bin2, FENG Guorui1, ZHANG Xinpeng1
Received:
2022-11-23
Published:
2024-09-29
CLC Number:
WANG Zichi, LI Bin, FENG Guorui, ZHANG Xinpeng. Review of Steganalysis for Digital Images[J]. Journal of Applied Sciences, 2024, 42(5): 723-732.
[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. |
[1] | WANG Na, ZHU Huijuan, SONG Xiangmei, FENG Xia. A Domain Adaptive Security Analysis Framework for Smart Contracts [J]. Journal of Applied Sciences, 2024, 42(4): 585-597. |
[2] | LI Zijie, ZHANG Shu, OUYANG Zhaoxiang, WANG Jun, WU Di. xDeepFM Recommendation Model Based on Field Factorization [J]. Journal of Applied Sciences, 2024, 42(3): 513-524. |
[3] | YUAN Ziye, QIU Baolin, YE Yu, WEN Wenying, HUA Dingli, ZHANG Yushu. A Robust Coverless Image Steganography Method for Coding Camouflage [J]. Journal of Applied Sciences, 2024, 42(3): 469-485. |
[4] | SHEN Kunye, ZHOU Xiaofei, FEI Xiaobo, CHEN Yuzhong, ZHANG Jiyong, YAN Chenggang. Boundary-Aware Deeply Residual Network for Salient Object Detection of Strip Steel Surface Defects [J]. Journal of Applied Sciences, 2023, 41(6): 978-988. |
[5] | ZHANG Chunsen, ZHU Jiangle, ZHANG Xuefen, LIU Xudong, SHI Shu. Void Filling of DEM in a Generative Adversarial Network Fused with Self-Attention Mechanism [J]. Journal of Applied Sciences, 2023, 41(5): 789-800. |
[6] | HUANG Xianpei, MENG Qingxiang. Land Cover Classification of Sentinel-2 Image Based on Multi-feature Convolution Neural Network [J]. Journal of Applied Sciences, 2023, 41(5): 766-776. |
[7] | WEI Jieling, MA Xiuli, JIN Yanliang, WANG Rui. Encrypted Traffic Classification Algorithm Based on VPN Channel [J]. Journal of Applied Sciences, 2023, 41(4): 646-656. |
[8] | LIU Zhaozhi, ZHAO Yan. A Mosaic Puzzle Camouflage Steganography with Image Block Rotation [J]. Journal of Applied Sciences, 2023, 41(2): 311-325. |
[9] | XIAO Xiaotong, DING Jianwei, ZHANG Qi. Segmented Backdoor Defense Based on Local Gradient and Global Gradient Ascent [J]. Journal of Applied Sciences, 2023, 41(2): 218-227. |
[10] | LIU Yalei, HE Hongjie, CHEN Fan, LIU Zhuohua. Traceable DNN Model Protection Based on Watermark Neural Network [J]. Journal of Applied Sciences, 2023, 41(2): 183-196. |
[11] | ZENG Jing, LI Ying, QI Xiaosha, JI Genlin. Video Anomaly Detection Method Based on Secondary Prediction of Multi-layer Memory Enhancement Generative Adversarial Network [J]. Journal of Applied Sciences, 2023, 41(1): 80-94. |
[12] | WANG Ting, WANG Na, CUI Yunpeng, LIU Juan. Medical Electronic Data Feature Learning Method Based on Deep Learning [J]. Journal of Applied Sciences, 2023, 41(1): 41-54. |
[13] | JIANG Xiaoyong, LI Zhongyi, HUANG Langyue, PENG Mengle, XU Shuyang. Review of Neural Network Pruning Techniques [J]. Journal of Applied Sciences, 2022, 40(5): 838-849. |
[14] | LUO Changyin, CHEN Xuebin, SONG Shangwen, ZHANG Shufen, LIU Zhiyu. Federated Ensemble Algorithm Based on Deep Neural Network [J]. Journal of Applied Sciences, 2022, 40(3): 493-510. |
[15] | NI Cui, WANG Peng, SUN Hao, LI Qian. An Improved ORB Algorithm Based on Quad-Tree Partition [J]. Journal of Applied Sciences, 2022, 40(2): 266-278. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||