Paper Info
Title
An Hevc Steganalytic Approach Against Motion Vector Modification Using Local Optimality In Candidate List
Abstract
Current research of video steganalysis is mainly oriented to H.264 or even earlier formats. As the H.265/HEVC standard is becoming more and more popular, there have been steganographic methods specifically designed for HEVC videos. Previous steganalytic methods are no longer effective due to the new features of HEVC. The advanced motion vector prediction technique employed by HEVC provides a new way for motion vector (MV) modification, utilizing the index of the candidate MV list. Such modification cannot be detected by previous steganalytic methods because the value of MV is not changed. To solve this problem, we proposed a new steganalytic strategy employing not only the local optimality of MV but also the local optimality in the candidate list. Combining the two types of local optimality, a 40 dimensional feature set is designed. Extensive experiments are carried out to evaluate the effectiveness of the proposed feature set. It is shown that compared with three current steganalyzers, our approach improves the performance against typical MV modification methods under various settings. In particular, our approach significantly boosts the detection accuracy of the index modification method that is exclusive to HEVC videos. To our best knowledge, this is the first work of video steganalytic method exploiting the new features of HEVC format.(c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.patrec.2021.02.018
PATTERN RECOGNITION LETTERS
Keywords
DocType
Volume
Video steganalysis, Motion vector, Local optimality, Candidate MV list
Journal
146
ISSN
Citations 
PageRank 
0167-8655
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Shuowei Liu101.35
Yongjian Hu292.82
Bei-Bei Liu312.37
Chang-Tsun Li4245.11