Steganalysis for HEVC video based on multi-scale residual convolution network

被引：0
|
作者：
Zhang M. [1 ]
Li Z. [2 ]
Liu J. [2 ]
Zhang Z. [3 ]
机构：
[1] China Telecom Corporation Limited, Beijing
[2] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing
[3] School of Information Engineering, Beijing Institute of Graphic Communication, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 47卷 / 11期
关键词：
Convolution network; Deep learning; HEVC; Steganalysis; Video;
D O I：
10.13700/j.bh.1001-5965.2021.0179
中图分类号：
学科分类号：
摘要：
The information exchange, in the forms of pictures, voice, video and other multimedia, plays an important role in network communication, as well as many illegal information disseminations are hidden. Steganalysis is an effective way of detecting secret information. This paper proposes a universal HEVC video steganalysis algorithm based on multi-scale residual convolution network, mainly consisting of residual calculation, feature extraction and binary classification. In the feature extraction part, residual convolution layer, multi-scale residual convolution module and a steganalysis residual block are proposed. Our experimental results show that the detection rate of this method based on video pixel domain analysis network is as high as 99.75%, which has greater advantages than the traditional manual feature extraction methods. © 2021, Editorial Board of JBUAA. All right reserved.
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页码：2226 / 2233
页数：7
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