基于多尺度残差卷积网络的HEVC视频隐写分析

张敏; 李赵红; 刘金豆; 张珍珍

doi:10.13700/j.bh.1001-5965.2021.0179

基于多尺度残差卷积网络的HEVC视频隐写分析

doi: 10.13700/j.bh.1001-5965.2021.0179

1.
中国电信股份有限公司, 北京 100010
2.
北京交通大学电子信息工程学院, 北京 100044
3.
北京印刷学院信息工程学院, 北京 102600

基金项目:

北京市教育委员会科研计划 KM202110015004

详细信息

通讯作者:
张敏, E-mail: zhangmin3@chinatelecom.cn

中图分类号: V221⁺.3;TB553
计量
- 文章访问数: 300
- HTML全文浏览量: 70
- PDF下载量: 118
- 被引次数: 0
出版历程
- 收稿日期: 2021-04-07
- 录用日期: 2021-05-09
- 网络出版日期: 2021-11-20

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

1.
China Telecom Corporation Limited, Beijing 100010, China
2.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
3.
School of Information Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China

Funds:

The Scientific Research Common Program of Beijing Municipal Commission of Education KM202110015004

More Information

Corresponding author: ZHANG Min, E-mail: zhangmin3@chinatelecom.cn

摘要

摘要:
图片、语音、视频等多媒体形式的信息交流在网络通信中占有重要地位，同时也有很多非法信息的传播隐匿于此。隐写分析是甄别隐秘信息是否存在的有效手段，提出了一种通用的基于多尺度残差卷积网络的HEVC视频隐写分析算法。网络主体由残差计算、特征提取和二分类3部分构成，其中在特征提取部分针对性地提出了残差卷积层、多尺度残差卷积模块及隐写分析残差块。实验结果表明：所提算法基于视频像素域分析网络的检测率高达99.75%，比传统的手工提取特征方法具有更大的优势。
- 隐写分析 /
- HEVC /
- 深度学习 /
- 视频 /
- 卷积网络
Abstract:
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.
- steganalysis /
- HEVC /
- deep learning /
- video /
- convolution network

HTML全文

图 1 帧间预测阶段的PU划分模式

Figure 1. PU partition modes during inter-frame prediction

下载: 全尺寸图片幻灯片

图 2 隐写视频像素域变化分析

Figure 2. Analysis of change in pixel domain of a steganographic video

下载: 全尺寸图片幻灯片

图 3 多尺度隐写分析网络结构

Figure 3. Multi-scale steganalysis network structure

下载: 全尺寸图片幻灯片

图 4 TanH的函数图像

Figure 4. Function graph of TanH

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图 5 多尺度残差卷积模块

Figure 5. Multi-scale residual convolution module

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图 6 隐写分析残差块

Figure 6. Steganalysis residual block

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图 7 隐写分析基础网络

Figure 7. BaseNet for steganalysis

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图 8 添加残差卷积层的隐写分析网络

Figure 8. Steganalysis network with residual convolution layer

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图 9 不使用隐写分析残差块的网络结构

Figure 9. Network structure without steganalysis residual block

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表 1 PU划分类型与整数的映射规则

Table 1. PU partition types and integer mapping rule

PU划分类型映射整数

2N×2N 0

N×N 1

N×2N 2

2N×N 3

nL×2N 4

nR×2N 5

2N×nU 6

2N×nD 7

下载: 导出CSV

表 2 实验结果对比

Table 2. Comparison of experimental results

算法检测率/%

CGPD传统特征 91.36

NRCNN 99.03

MSSN 99.75

下载: 导出CSV

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