Information Sciences: an International Journal
Steganalysis by subtractive pixel adjacency matrix
IEEE Transactions on Information Forensics and Security
Expert Systems with Applications: An International Journal
Blind quantitative steganalysis based on feature fusion and gradient boosting
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Progressive exponential clustering-based steganography
EURASIP Journal on Advances in Signal Processing
Moving steganography and steganalysis from the laboratory into the real world
Proceedings of the first ACM workshop on Information hiding and multimedia security
Steganalysis of F5-like steganography based on selection of joint distribution features
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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The aim of this paper is to construct a practical forensic steganalysis tool for JPEG images that can properly analyze single- and double-compressed stego images and classify them to selected current steganographic methods. Although some of the individual modules of the steganalyzer were previously published by the authors, they were never tested as a complete system. The fusion of the modules brings its own challenges and problems whose analysis and solution is one of the goals of this paper. By determining the stego-algorithm, this tool provides the first step needed for extracting the secret message. Given a JPEG image, the detector assigns it to six popular steganographic algorithms. The detection is based on feature extraction and supervised training of two banks of multiclassifiers realized using support vector machines. For accurate classification of single-compressed images, a separate multiclassifier is trained for each JPEG quality factor from a certain range. Another bank of multiclassifiers is trained for double-compressed images for the same range of primary quality factors. The image under investigation is first analyzed using a preclassifier that detects selected cases of double compression and estimates the primary quantization table. It then sends the image to the appropriate single- or double-compression multiclassifier. The error is estimated from more than 2.6 million images. The steganalyzer is also tested on two previously unseen methods to examine its ability to generalize.