[1]张涛,葛育伟,韩旭,等.基于对抗机制的彩色图像隐写分析算法[J].郑州大学学报(工学版),2023,44(04):10-15.[doi:10.13705/j.issn.1671-6833.2023.04.013]
 ZHANG Tao,GE Yuwei,HAN Xu,et al.Color Image Steganalysis Algorithm Based on Adversarial Mechanisms[J].Journal of Zhengzhou University (Engineering Science),2023,44(04):10-15.[doi:10.13705/j.issn.1671-6833.2023.04.013]
点击复制

基于对抗机制的彩色图像隐写分析算法()
分享到:

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44卷
期数:
2023年04期
页码:
10-15
栏目:
出版日期:
2023-06-01

文章信息/Info

Title:
Color Image Steganalysis Algorithm Based on Adversarial Mechanisms
作者:
张涛1葛育伟2韩旭2张昊1汪然1
1.战略支援部队信息工程大学 信息系统工程学院,河南 郑州 450001, 2.苏州大学 计算机科学与技术学院,江苏苏州 215006

Author(s):
ZHANG Tao1 GE Yuwei2 HAN Xu2 ZHANG Hao1 WANG Ran1
1.School of Information System Engineering, the University of Strategic Support Forces Information Engineering University, 450001, 2.Zhengzhou, Henan, School of Computer Science and Technology, Suzhou University, Suzhou, Jiangsu 215006
关键词:
信息隐藏 隐写分析 深度学习 多激活模块 对抗机制
Keywords:
information hidding steganalysis deep learning multiple activation modules adversarial mechanisms
分类号:
TP391;TN915.08;O235
DOI:
10.13705/j.issn.1671-6833.2023.04.013
文献标志码:
A
摘要:
针对彩色图像的隐写分析问题,引入逐通道卷积、多激活模块以及对抗机制,提出了一种应用于彩色图像 隐写分析的深度卷积网络。 逐通道卷积能够避免削弱不相关噪声信号,保留更多的隐写嵌入特征;多激活模块利 用多种激活函数对卷积结果进行非线性映射,针对嵌入痕迹做出不同反馈,丰富嵌入特征的多样表达;对抗机制能 够将内容信息特征和隐写嵌入特征从域类别上严格划分,从而分离出更多的隐写存在性特征。 在 PPG-LIRMMCOLOR 数据集上针对 多 种 隐 写 算 法 进 行 了 检 测 实 验。 结 果 显 示,所 提 算 法 比 对 照 方 法 中 性 能 最 好 的 还 要 高 1. 83%到 4. 99%。 实验结果验证了该彩色图像隐写分析方法的有效性。
Abstract:
Aiming at the steganalysis of color images, a deep convolutional network applied to the steganalysis of color images is proposed by introducing channel-wise convolution, multiple activation module and adversarial mechanism. Channel-wise convolution can avoid weakening irrelevant noise signals and retain additional steganographic embedded features; multiple activation modules use various activation functions to nonlinearly map convolution results and make different feedback for embedded traces to enrich the diverse expressions of embedded features; adversarial mechanisms can divide content information features and steganographic embedding features from domain categories, thereby separating additional steganographic existence features. Experiments are carried out on the PPG-LIRMM-COLOR dataset for various steganographic algorithms. The proposed algorithm is 1. 83% - 4. 99% higher performance than the best performance in the control methods. Results verify the effectiveness of the proposed color image steganalysis method.

参考文献/References:

[1] FILLER T, FRIDRICH J. Gibbs construction in ste-ganography[J]. IEEE Transactions on Information Forensics and Security, 2010, 5(4): 705-720.

[2] 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.
[3] HOLUB V, FRIDRICH J. Designing steganographic distortion using directional filters[C]∥2012 IEEE International Workshop on Information Forensics and Security (WIFS). Piscataway: IEEE, 2013: 234-239.
[4] HOLUB V, FRIDRICH J, DENEMARK T. Universal distortion function for steganography in an arbitrary domain[J].EURASIP Journal on Information Security, 2014, 2014(1): 1-13.
[5] 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). Piscataway: IEEE, 2015: 4206-4210.
[6] TANG W X, LI B, LUO W Q, et al. Clustering steganographic modification directions for color components[J]. IEEE Signal Processing Letters, 2016, 23(2): 197-201.
[7] GOLJAN M, FRIDRICH J, COGRANNE R. Rich model for steganalysis of color images[C]∥2014 IEEE International Workshop on Information Forensics and Security (WIFS). Piscataway: IEEE, 2015: 185-190.
[8] ABDULRAHMAN H, CHAUMONT M, MONTESINOS P, et al. Color image steganalysis based on steerable Gaussian filters bank[C]∥Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. New York: ACM, 2016: 109-114.
[9] ABDULRAHMAN H, CHAUMONT M, MONTESINOS P, et al. Color image stegananalysis using correlations between RGB channels[C]∥2015 10th International Conference on Availability, Reliability and Security. Piscataway: IEEE, 2015: 448-454.
[10] TAN S Q, LI B. Stacked convolutional auto-encoders for steganalysis of digital images[C]∥Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific. Piscataway: IEEE, 2015: 1-4.
[11] QIAN Y L, DONG J, WANG W, et al. Deep learning for steganalysis via convolutional neural networks[C]∥Conference on Media Watermarking, Security, and Forensics. Palos Verdes:SPIE, 2015:171-180.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] YEDROUDJ M, COMBY F, CHAUMONT M. Yedroudj-Net: an efficient CNN for spatial steganalysis[C]∥2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2018: 2092-2096.
[17] HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE, 2020: 1314-1324.
[18] SU K, KUNDUR D, HATZINAKOS D. Statistical invisibility for collusion-resistant digital video watermarking[J]. IEEE Transactions on Multimedia, 2005, 7(1): 43-51.
[19] 张坚鑫, 郭四稳, 张国兰, 等. 基于多尺度特征融合的火灾检测模型[J]. 郑州大学学报(工学版), 2021, 42(5): 13-18.ZHANG J X, GUO S W, ZHANG G L, et al. Fire detection model based on multi-scale feature fusion[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(5): 13-18.
[20] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2015: 1-9.

更新日期/Last Update: 2023-06-30