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Asymptotically optimal detection of LSB matching data hiding

Abstract : This paper proposes a novel method, based on hypothesis testing theory, to detect data hidden with the LSB matching. When all the image parameters, a test which asymptotically maximizes the detection power and guarantees a false-alarm probability, is presented and its statistical properties are analytically given in a closed-form. This provides an asymptotic upper-bound for the power of any detector for LSB matching. In practice the image parameters are unknown. A Generalized Likelihood Ratio Test (GLRT) is proposed and its statistical properties are also analytically established. Numerical results and comparisons with prior art detectors highlight the relevance of the proposed methodology.
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Submitted on : Wednesday, October 2, 2019 - 11:59:23 AM
Last modification on : Thursday, October 3, 2019 - 1:27:36 AM





Rémi Cogranne, Thanh Hai Thai, Florent Retraint. Asymptotically optimal detection of LSB matching data hiding. 2013 20th IEEE International Conference on Image Processing (ICIP), Sep 2013, Melbourne, Australia. pp.4437-4441, ⟨10.1109/ICIP.2013.6738914⟩. ⟨hal-02303425⟩



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