华南理工大学学报(自然科学版) ›› 2020, Vol. 48 ›› Issue (8): 1-9.doi: 10.12141/j.issn.1000-565X.190917

• 电子、通信与自动控制 •    下一篇

针对视频运动向量隐写的深度神经网络检测方法

黄雄波1,胡永健1,王宇飞2   

  1. 1. 华南理工大学 电子与信息学院,广东 广州 510640; 2. 中新国际联合研究院,广东 广州 510700
  • 收稿日期:2019-12-19 修回日期:2020-03-13 出版日期:2020-08-25 发布日期:2020-08-01
  • 通信作者: 胡永健(1962-),男,教授,博士生导师,主要从事多媒体信息安全、图像处理与人工智能研究。 E-mail:eeyjhu@scut.edu.cn
  • 作者简介:黄雄波(1975-),博士生,主要从事多媒体信息安全、图像处理及模式识别研究。E-mail:xb-Huang@hot-mail.com
  • 基金资助:
    国家重点研发计划项目 (2019QY2202); 广东省科技计划国际协同创新项目 (2017A050501002); 中新国际联合研究院项目 (206-A017023,206-A018001); 广州市产业技术重大攻关计划项目 (201902010028)

A Detection Method with Deep Neural Networks for Video Motion Vector Steganography

HUANG Xiongbo1 HU Yongjian 1,2 WANG Yufei2   

  1. 1. School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China; 2. Sino-Singapore International Joint Research Institute,Guangzhou 510700,Guangdong,China
  • Received:2019-12-19 Revised:2020-03-13 Online:2020-08-25 Published:2020-08-01
  • Contact: 胡永健(1962-),男,教授,博士生导师,主要从事多媒体信息安全、图像处理与人工智能研究。 E-mail:eeyjhu@scut.edu.cn
  • About author:黄雄波(1975-),博士生,主要从事多媒体信息安全、图像处理及模式识别研究。E-mail:xb-Huang@hot-mail.com
  • Supported by:
    Supported by the National Key R&D Project of China (2019QY2202) and the Science and Technology Planning Project for International Collaborative Innovation Program of Guangdong Province (2017A050501002)

摘要: 现有的深度神经网络隐写检测技术主要针对数字图像隐写,但视频隐写与图像隐写存在很大差异,因此无法将用于图像隐写分析的深度神经网络直接用于视频隐写分析。为此,文中以修改运动向量的视频隐写为检测对象,在新型图像隐写分析网络SRNet基础上,设计了一种用于视频隐写检测的深度神经网络,构造了能准确反映运动向量隐写修改的输入矩阵。实验结果表明,文中提出的方法对中低码率的视频检测准确率明显高于两种传统的视频隐写分析方法,且对不同码率的视频检测性能平稳。

关键词: 视频隐写分析, 神经网络, 运动向量, 数据输入矩阵, 双路输入网络

Abstract: The existing deep neural network steganography detection technology was mainly proposed for digital image steganography. Since there are great differences between image steganography and video steganography,the deep neural networks designed for image steganalysis cannot be simply extended to video steganalysis. Therefore,a motion vector-modification video steganography was taken as the target example and a deep neural network for video steganalysis was proposed based on state-of-the-art image steganalysis network SRNet. A data input matrix which can well reflect the steganographic modification of motion vectors was also introduced. Experimental results demon-strate that the proposed method outperforms two traditional video steganalysis algorithms in detection accuracy for low and middle-bitrate videos,and behaves well for videos at different bitrates.

Key words: video steganalysis, neural networks, motion vector, data input matrix, two-input network

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