Asymptotically optimal detection of LSB matching data hiding - Archive ouverte HAL Access content directly
Conference Papers Year :

Asymptotically optimal detection of LSB matching data hiding

(1) , (1) , (1)
1

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.
Not file

Dates and versions

hal-02303425 , version 1 (02-10-2019)

Identifiers

Cite

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⟩
11 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More