Diversity-Based Cascade Filters for JPEG Steganalysis

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.

[1]  Guanshuo Xu,et al.  Deep Convolutional Neural Network to Detect J-UNIWARD , 2017, IH&MMSec.

[2]  Xinpeng Zhang,et al.  Toward Construction-Based Data Hiding: From Secrets to Fingerprint Images , 2019, IEEE Transactions on Image Processing.

[3]  Jessica J. Fridrich,et al.  Steganalysis Features for Content-Adaptive JPEG Steganography , 2016, IEEE Transactions on Information Forensics and Security.

[4]  Yi Ma,et al.  Improving GFR Steganalysis Features by Using Gabor Symmetry and Weighted Histograms , 2017, IH&MMSec.

[5]  Yun Q. Shi,et al.  Uniform Embedding for Efficient JPEG Steganography , 2014, IEEE Transactions on Information Forensics and Security.

[6]  Siwei Lyu,et al.  Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines , 2002, Information Hiding.

[7]  Ying Zeng,et al.  Pixel Group Trace Model-Based Quantitative Steganalysis for Multiple Least-Significant Bits Steganography , 2013, IEEE Transactions on Information Forensics and Security.

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  Stefanos Gritzalis,et al.  On the Typical Statistic Features for Image Blind Steganalysis , 2011, IEEE Journal on Selected Areas in Communications.

[10]  Guorui Feng,et al.  Deep learning for steganalysis based on filter diversity selection , 2018, Science China Information Sciences.

[11]  Xinpeng Zhang,et al.  JPEG Steganalysis With High-Dimensional Features and Bayesian Ensemble Classifier , 2013, IEEE Signal Processing Letters.

[12]  Jessica J. Fridrich,et al.  Universal distortion function for steganography in an arbitrary domain , 2014, EURASIP Journal on Information Security.

[13]  Guorui Feng,et al.  Image Steganalysis via Random Subspace Fisher Linear Discriminant Vector Functional Link Network and Feature Mapping , 2018, Mobile Networks and Applications.

[14]  Xinpeng Zhang,et al.  Towards Robust Image Steganography , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Jessica J. Fridrich,et al.  Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT , 2015, IEEE Transactions on Information Forensics and Security.

[16]  Huan Liu,et al.  Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..

[17]  Oleksandr Makeyev,et al.  Neural network with ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[18]  Weiming Zhang,et al.  Decomposing Joint Distortion for Adaptive Steganography , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Jessica J. Fridrich,et al.  Steganalysis of JPEG images using rich models , 2012, Other Conferences.

[20]  Yi Zhang,et al.  Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters , 2015, IH&MMSec.

[21]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Yun Q. Shi,et al.  A Markov Process Based Approach to Effective Attacking JPEG Steganography , 2006, Information Hiding.

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

[24]  Guo-Shiang Lin,et al.  A feature-based classification technique for blind image steganalysis , 2005, IEEE Transactions on Multimedia.

[25]  Yun Q. Shi,et al.  Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited , 2015, IEEE Transactions on Information Forensics and Security.