Pure spatial rich model features for digital image steganalysis

被引:0
|
作者
Pengfei Wang
Zhihui Wei
Liang Xiao
机构
[1] Nanjing University of Science and Technology,School of Computer Science and Engineering
[2] Anhui University of Technology,School of Computer Science and Technology
来源
关键词
Steganalysis; Content adaptive steganography; Pure SRM features; Neighboring noise residual sample selection; Multi-order statistical features;
D O I
暂无
中图分类号
学科分类号
摘要
The SRM (Spatial Rich Model) is a very effective steganalysis method. It uses statistics of neighboring noise residual samples as features to capture the dependency changes caused by embedding. Because the noise residuals are the high-frequency components of image and closely tied to image content, the residuals of different types of image regions have different statistical properties and effectiveness for steganalysis. In this paper, the effectiveness of the residuals is investigated. Then the effectiveness of the statistics collected from different types of neighboring residual samples is investigated from the FLD (Fisher Linear Discriminant) viewpoint, and ineffective, effective and high-effective neighboring residual samples are defined. The ineffective neighboring residual samples are not likely to change during embedding, and if they are counted in statistics, they may mix the features with noise and make the features impure. Pure SRM features are extracted based on neighboring noise residual sample selection strategy. Furthermore, multi-order statistical features are proposed to increase the statistical diversity. Steganalysis performances of the statistical features collected from different types of neighboring residual samples are investigated on three content adaptive steganographic algorithms. Experimental results demonstrate that the proposed method can achieve a more accurate detection than SRM.
引用
收藏
页码:2897 / 2912
页数:15
相关论文
共 50 条
  • [1] Pure spatial rich model features for digital image steganalysis
    Wang, Pengfei
    Wei, Zhihui
    Xiao, Liang
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2016, 75 (05) : 2897 - 2912
  • [2] Fast projections of spatial rich model feature for digital image steganalysis
    Wang, Pengfei
    Wei, Zhihui
    Xiao, Liang
    [J]. SOFT COMPUTING, 2017, 21 (12) : 3335 - 3343
  • [3] Fast projections of spatial rich model feature for digital image steganalysis
    Pengfei Wang
    Zhihui Wei
    Liang Xiao
    [J]. Soft Computing, 2017, 21 : 3335 - 3343
  • [4] Deep Learning on Spatial Rich Model for Steganalysis
    Xu, Xiaoyu
    Sun, Yifeng
    Tang, Guangming
    Chen, Shiyuan
    Zhao, Jian
    [J]. DIGITAL FORENSICS AND WATERMARKING, IWDW 2016, 2017, 10082 : 564 - 577
  • [5] Image textural features for steganalysis of spatial domain steganography
    Xiong, Gang
    Ping, Xijian
    Zhang, Tao
    Hou, Xiaodan
    [J]. JOURNAL OF ELECTRONIC IMAGING, 2012, 21 (03)
  • [6] A new JPEG image steganalysis technique combining rich model features and convolutional neural networks
    Zhang, Tao
    Zhang, Hao
    Wang, Ran
    Wu, Yunda
    [J]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (05) : 4069 - 4081
  • [7] Steganalysis of spatial image combining fusion features and feature mapping
    Luo, Wei-Wei
    Liu, Shao-Wei
    Zhang, Bing-Tao
    Li, Meng
    Liu, Hai-Luan
    Fan, Ling-Yan
    [J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2023, 53 (11): : 3260 - 3267
  • [8] Optimization of Rich model based on Fisher criterion for image steganalysis
    Zhang, Yiqin
    Liu, Fenlin
    Jia, Hongyan
    Lu, Jicang
    Yang, Chunfang
    [J]. PROCEEDINGS OF 2018 TENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2018, : 187 - 192
  • [9] Understanding Multi-layer Perceptrons on Spatial Image Steganalysis Features
    Zheng, Lilei
    Zhang, Ying
    Thing, Vrizlynn L. L.
    [J]. 2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017), 2017, : 1035 - 1039
  • [10] The Challenges of Rich Features in Universal Steganalysis
    Pevny, Tomas
    Ker, Andrew D.
    [J]. MEDIA WATERMARKING, SECURITY, AND FORENSICS 2013, 2013, 8665