 |
Conference papers
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.
https://hal-utt.archives-ouvertes.fr/hal-02303425
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Wednesday, October 2, 2019 - 11:59:23 AM
Last modification on : Thursday, October 3, 2019 - 1:27:36 AM
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Wednesday, October 2, 2019 - 11:59:23 AM
Last modification on : Thursday, October 3, 2019 - 1:27:36 AM
