From Blind to Quantitative Steganalysis

• Published in: IEEE transactions on information forensics and security Vol. 7; no. 2; pp. 445 - 454
• Main Authors: Pevny, T., Fridrich, J., Ker, A. D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Blind steganalysis; Blinds; Construction; Feature extraction; Forensic engineering; Kernel; Mathematical analysis; message length estimation; Messages; Q factor; quantitative steganalysis; Regression; Support vector machines; Training; Transform coding; Vectors (mathematics)
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2011.2175918

A quantitative steganalyzer is an estimator of the number of embedding changes introduced by a specific embedding operation. Since for most algorithms the number of embedding changes correlates with the message length, quantitative steganalyzers are important forensic tools. In this paper, a general method for constructing quantitative steganalyzers from features used in blind detectors is proposed. The core of the method is a support vector regression, which is used to learn the mapping between a feature vector extracted from the investigated object and the embedding change rate. To demonstrate the generality of the proposed approach, quantitative steganalyzers are constructed for a variety of steganographic algorithms in both JPEG transform and spatial domains. The estimation accuracy is investigated in detail and compares favorably with state-of-the-art quantitative steganalyzers.