IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
Review: A review on blind detection for image steganography
Signal Processing
Steganalysis by subtractive pixel adjacency matrix
IEEE Transactions on Information Forensics and Security
Neighboring joint density-based JPEG steganalysis
ACM Transactions on Intelligent Systems and Technology (TIST)
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Optimized Feature Extraction for Learning-Based Image Steganalysis
IEEE Transactions on Information Forensics and Security
Multiclass Detector of Current Steganographic Methods for JPEG Format
IEEE Transactions on Information Forensics and Security
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For steganalysis of F5-like steganography with two types of widely used joint distribution statistical features: co-occurrence matrix and Markov transition probability matrix, a feature selection and fusion method based on separability comparison is proposed in this paper. Firstly, the changing ways of F5-like steganography to image data is analyzed. Then, according to different affecting ways of the changing to two types of features, the separability of each feature component is analyzed and compared. At last, based on the comparison conclusion, a new feature is obtained by selection and fusion. Experimental results show that, compared with existing typical features, the proposed new feature can achieve higher steganalytic accuracy and enhance the detection reliability.