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.
Document type :
Conference papers
Complete list of metadatas

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

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

6