视频对象移除篡改的时空域定位被动取证  

Passive forensic based on spatio-temporal localization of video object removal tampering

作　　者：陈临强[1] 杨全鑫 袁理锋 姚晔[1] 张祯[1] 吴国华[1] CHEN Linqiang;YANG Quanxin;YUAN Lifeng;YAO Ye;ZHANG Zhen;WU Guohua(School of Cyberspace Security,Hangzhou Dianzi University,Hangzhou 310018,China;School of Computer,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学网络空间安全学院,浙江杭州310018 [2]杭州电子科技大学计算机学院,浙江杭州310018

出　　处：《通信学报》2020年第7期110-120,共11页Journal on Communications

基　　金：教育部人文社科基金资助项目(No.17YJC870021)。

摘　　要：针对视频被动取证领域中视频内容的真实性和完整性鉴定及篡改区域定位问题,提出了一种基于视频噪声流的深度学习检测算法。首先,构建了基于空间富模型(SRM)和三维卷积(C3D)神经网络的特征提取器、帧鉴别器和基于区域建议网络(RPN)思想的空域定位器;其次,将特征提取器分别与帧鉴别器和空域定位器相结合,搭建出2个神经网络;最后,利用增强处理后的数据训练出2种深度学习模型,分别用于对视频篡改区域时域和空域的定位。测试结果表明,时域定位的准确率提高到98.5%,空域定位与篡改区域标注平均交并比达49%,可以有效对该类篡改视频进行篡改区域时空域定位。To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM(spatial rich model)and C3D(3D convolution)neural network,a feature extractor,a frame discriminator and a RPN(region proposal network)based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.

关 键 词：视频对象移除篡改 时空域定位 视频被动取证 三维卷积目标检测 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]