An Asymptotically Uniformly Most Powerful Test for LSB Matching Detection

• Published in: IEEE transactions on information forensics and security Vol. 8; no. 3; pp. 464 - 476
• Main Authors: Cogranne, R., Retraint, F.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Asymptotic properties; Computer aided design; Computer information security; Computer Science; Computer science; control theory; systems; Cryptography; Cryptography and Security; Detectors; Digital; Digital broadcasting; Estimation; Exact sciences and technology; Hypothesis testing theory; information forensics; information hiding; Information systems. Data bases; Information, signal and communications theory; Likelihood ratio; Machine learning; Matching; Mathematical analysis; Mathematical models; Media; Memory and file management (including protection and security); Memory organisation. Data processing; nuisance parameters; optimal detection; Probability; Signal and communications theory; Software; Studies; Telecommunications and information theory; Testing; Encapsulation; Parameter estimation; Nuisance parameter; Software development; Local theory; Very large databases; Hypothesis testing theory; information forensics; Modeling; Relevance; Hypothesis test; Statistical test; System identification; Computer security; Multimedia; optimal detection; Probabilistic approach; Forensic science; information hiding; Stéganography; False alarm rate; Upper bound; Likelihood ratio test; Asymptotic approximation; nuisance parameters
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2013.2238232

This paper investigates the detection of information hidden in digital media by the least significant bit (LSB) matching scheme. In a theoretical context of known medium parameters, two important results are presented. First, based on the likelihood ratio test, we present a test that asymptotically maximizes the detection power whatever the embedding rate might be. Second, the statistical properties of this test are analytically calculated; it is particularly shown that the decision threshold which warrants a given probability of false-alarm is independent of inspected medium parameters. This provides an asymptotic upper-bound for the detection power of any test that aims at detecting data hidden with the LSB matching method. In practice, when detecting LSB matching, the unknown medium parameters have to be estimated. Based on a local model of digital media, a generalized likelihood ratio test is presented by replacing the unknown parameters by their estimation. Numerical results on large databases highlight the relevance of the proposed methodology and comparison with state-of-the-art detectors shows that the proposed tests perform well.