24 February 2017 On the probability density function and characteristic function moments of image steganalysis in the log prediction error wavelet subband
Author Affiliations +
Abstract
Extracting informative statistic features is the most essential technical issue of steganalysis. Among various steganalysis methods, probability density function (PDF) and characteristic function (CF) moments are two important types of features due to the excellent ability for distinguishing the cover images from the stego ones. The two types of features are quite similar in definition. The only difference is that the PDF moments are computed in the spatial domain, while the CF moments are computed in the Fourier-transformed domain. Then, the comparison between PDF and CF moments is an interesting question of steganalysis. Several theoretical results have been derived, and CF moments are proved better than PDF moments in some cases. However, in the log prediction error wavelet subband of wavelet decomposition, some experiments show that the result is opposite and lacks a rigorous explanation. To solve this problem, a comparison result based on the rigorous proof is presented: the first-order PDF moment is proved better than the CF moment, while the second-order CF moment is better than the PDF moment. It tries to open the theoretical discussion on steganalysis and the question of finding suitable statistical features.
© 2017 SPIE and IS&T
Zhen-kun Bao, Xiaolong Li, Xiangyang Luo, "On the probability density function and characteristic function moments of image steganalysis in the log prediction error wavelet subband," Journal of Electronic Imaging 26(1), 013025 (24 February 2017). https://doi.org/10.1117/1.JEI.26.1.013025 . Submission: Received: 20 December 2016; Accepted: 9 February 2017
Received: 20 December 2016; Accepted: 9 February 2017; Published: 24 February 2017
JOURNAL ARTICLE
7 PAGES


SHARE
Back to Top