Binary Image Steganalysis Based on Histogram of Structuring Elements

Utilizing statistical models of binary images is a common and effective means to steganalyze binary images, and the design of the statistical model is essential to the performance of steganalysis. In this paper, we propose a new model based on a histogram of pixel structuring elements (SEs), which is a suitable representation of a binary image for the task of steganalysis. The texture property and the dependency among pixels are considered inside the SEs. The SEs with different patterns will be evaluated comprehensively according to a statistical criterion, and some of them will be selected to construct the feature set for training the steganalyzer. The distributions of these selected SEs, which contain many highly flippable pixels, will be emphasized by the criterion, and they can reflect the difference between cover images and stego-images. Finally, a series of experiments are conducted on two datasets, and the results show that the proposed scheme significantly outperforms state-of-the-art schemes.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Jessica J. Fridrich,et al.  Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.

[3]  Jiangqun Ni,et al.  Deep Learning Hierarchical Representations for Image Steganalysis , 2017, IEEE Transactions on Information Forensics and Security.

[4]  Alex ChiChung Kot,et al.  Pattern-Based Data Hiding for Binary Image Authentication by Connectivity-Preserving , 2007, IEEE Transactions on Multimedia.

[5]  Wei Lu,et al.  Secure Binary Image Steganography Based on Fused Distortion Measurement , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Hongbin Zhang,et al.  High Capacity Data Hiding for Binary Image Authentication , 2010, 2010 20th International Conference on Pattern Recognition.

[7]  Jessica J. Fridrich,et al.  Steganalysis in high dimensions: fusing classifiers built on random subspaces , 2011, Electronic Imaging.

[8]  Mo Chen,et al.  Deep Residual Network for Steganalysis of Digital Images , 2019, IEEE Transactions on Information Forensics and Security.

[9]  Wei Lu,et al.  Binary image steganalysis based on pixel mesh Markov transition matrix , 2015, J. Vis. Commun. Image Represent..

[10]  Jialiang Chen,et al.  Binary image steganalysis based on local texture pattern , 2018, J. Vis. Commun. Image Represent..

[11]  Susanto Rahardja,et al.  Orthogonal Data Embedding for Binary Images in Morphological Transform Domain- A High-Capacity Approach , 2008, IEEE Transactions on Multimedia.

[12]  Alex ChiChung Kot,et al.  On Establishing Edge Adaptive Grid for Bilevel Image Data Hiding , 2013, IEEE Transactions on Information Forensics and Security.

[13]  Wei Lu,et al.  Secure Binary Image Steganography Based on Minimizing the Distortion on the Texture , 2015, IEEE Transactions on Information Forensics and Security.

[14]  Shuo-zhong Wang,et al.  Steganography in binary image by checking data-carrying eligibility of boundary pixels , 2007 .

[15]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[16]  Jessica J. Fridrich,et al.  Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes , 2011, IEEE Transactions on Information Forensics and Security.

[17]  Xiangyang Luo,et al.  Selection of Rich Model Steganalysis Features Based on Decision Rough Set $\alpha$ -Positive Region Reduction , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[19]  J. Chhajed,et al.  Review on Binary Image Steganography and Watermarking , 2011 .

[20]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[21]  Josef Pieprzyk,et al.  Binary Image Steganographic Techniques Classification Based on Multi-class Steganalysis , 2010, ISPEC.

[22]  Wei Lu,et al.  Content-Adaptive Residual for Steganalysis , 2014, IWDW.

[23]  Jessica Fridrich,et al.  Applications of Explicit Non-Linear Feature Maps in Steganalysis , 2018, IEEE Transactions on Information Forensics and Security.

[24]  Fenlin Liu,et al.  Steganalysis Feature Subspace Selection Based on Fisher Criterion , 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[25]  Wei Lu,et al.  Binary Image Steganalysis Based on Distortion Level Co-occurrence Matrix , 2018 .

[26]  Josef Pieprzyk,et al.  Blind Steganalysis: A Countermeasure for Binary Image Steganography , 2010, 2010 International Conference on Availability, Reliability and Security.

[27]  Jian Weng,et al.  Steganalysis of content-adaptive binary image data hiding , 2017, J. Vis. Commun. Image Represent..

[28]  Bin Li,et al.  New Steganalytic Features for Spatial Image Steganography Based on Derivative Filters and Threshold LBP Operator , 2017, IEEE Transactions on Information Forensics and Security.

[29]  Tomás Pevný,et al.  Steganalysis by Subtractive Pixel Adjacency Matrix , 2009, IEEE Transactions on Information Forensics and Security.

[30]  Min Wu,et al.  Data hiding in binary image for authentication and annotation , 2004, IEEE Transactions on Multimedia.