From Blind to Quantitative Steganalysis
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...
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| Published in | IEEE transactions on information forensics and security Vol. 7; no. 2; pp. 445 - 454 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.04.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1556-6013 1556-6021 |
| DOI | 10.1109/TIFS.2011.2175918 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Fridrich, J. Pevny, T. Ker, A. D. |
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| SubjectTerms | 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) |
| Title | From Blind to Quantitative Steganalysis |
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