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杜振龙1, 焦丽鑫1, 李晓丽1, 郭延文2, 杨小健1(1.南京工业大学电子与信息工程学院, 南京 210009;2.南京大学软件新技术国家重点实验室, 南京 210000)

摘 要
目的 随着数字获取技术的发展,数字媒体文档的获取越来越方便,并已成为人们现代生活中不可缺少的组成部分。功能强大的视频编辑软件为视频复制粘贴提供了方便,因此视频伪造检测具有重大现实需求。利用传统的图像伪造检测算法逐帧对视频进行伪造检测计算量大、耗时冗长,且不能保证检测结果的时空一致性。方法 提出了一种基于稠密SIFT(scale invariant feature transform)流的帧内复制粘贴视频伪造盲检测算法。所提算法自适应地在内容最小变化帧位置把视频划分为多个视频段,提取每个视频段的关键帧;在关键帧利用匹配SIFT关键点定位初始疑似复制粘贴伪造区域,通过SIFT关键点和均值漂移分割区域的位置依赖关系细化疑似伪造区域;采用稠密SIFT流把关键帧检测结果过渡至非关键帧,最终实现视频的复制粘贴伪造盲检测。结果 实验结果表明,所提算法的检测效率比传统算法快了一个数量级,检测平均准确率比传统算法提高了约20%。结论 所提视频伪造盲检测方法能够高效地检测出帧内复制粘贴的视频伪造区域。
Intraframe copy-paste forgery video blind detection based on dense SIFT flow

Du Zhenlong1, Jiao Lixin1, Li Xiaoli1, Guo Yanwen2, Yang Xiaojian1(1.College of Electronics and Information Engineering, Nanjing University of Technology, Nanjing 210009, China;2.State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210000, China)

Objective With the rapid development of digital acquisition technology, media document are easily acquired and become an indispensable part of peoples' modern life. The powerful video editing software has made the video copy-paste forgery become more and more easy. Therefore, the appraisal of video authenticity has great significance. Direct extending the traditional image forgery detection algorithms to forgery video detection is computational expensive and time-consuming, moreover, the spatiotemporal consistency could not be preserved. Method In this paper, an intraframe copy-paste forgery video blind detection approach based on dense scale invariant feature transform(SIFT) flow is proposed. The proposed algorithm divides videos into sub-clips at the minimal content variation, extracts the keyframe as proxy frame. It detects the initial forgery region by matching SIFT keypoints, refines the forgery region by exploiting the SIFT key points dependence with mean shift region, and warps the keyframe detected region to the remaindering frames. Result The experiments showedthat the presented approach achieves one order of magnitude improvement in efficiency, and improves the mean detection accuracy by 20%. Conclusion The proposed video forgery detection algorithm could efficiently detect the copy-paste forged regions within the video.