公民新闻视频的真实性验证

Mahmudur Rahman, Mozhgan Azimpourkivi, Umut Topkara, Bogdan Carbunar
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引用次数: 5

摘要

公民新闻视频越来越多地通过事件目击者的直接报道来补充甚至取代专业新闻报道。这引发了对这些视频的完整性和可信度的质疑。我们推出首个基于视频运动的用户透明视频“活跃性”验证解决方案Vamos,可集成到任何移动视频捕获应用程序中,无需特殊的用户培训。Vamos的算法不仅可以适应摄像机的全方位运动,还可以支持任意长度的视频。我们通过利用全自动攻击者和雇用训练有素的人类专家创建欺诈性视频来挫败移动视频验证系统来开发强大的攻击。我们引入视频运动类别的概念来标注任意视频的摄像机和用户运动特征。我们分享YouTube公民新闻视频的动作注释,以及我们通过用户研究收集的自由格式视频样本。我们观察到Vamos的性能在不同的视频运动类别中是不同的。我们通过投射类别的分布来报告Vamos在真实公民新闻视频块上的预期性能。尽管Vamos是基于运动的,但我们观察到它对相对“静态”视频块的攻击具有令人惊讶的、看似违反直觉的弹性,这些视频块包含难以模仿的非自愿运动。研究表明,Vamos在验证全长视频的任务中,对新的攻击的准确率超过93%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Liveness verifications for citizen journalism videos
Citizen journalism videos increasingly complement or even replace the professional news coverage through direct reporting by event witnesses. This raises questions of the integrity and credibility of such videos. We introduce Vamos, the first user transparent video "liveness" verification solution based on video motion, that can be integrated into any mobile video capture application without requiring special user training. Vamos' algorithm not only accommodates the full range of camera movements, but also supports videos of arbitrary length. We develop strong attacks both by utilizing fully automated attackers and by employing trained human experts for creating fraudulent videos to thwart mobile video verification systems. We introduce the concept of video motion categories to annotate the camera and user motion characteristics of arbitrary videos. We share motion annotations of YouTube citizen journalism videos and of free-form video samples that we collected through a user study. We observe that the performance of Vamos differs across video motion categories. We report the expected performance of Vamos on the real citizen journalism video chunks, by projecting on the distribution of categories. Even though Vamos is based on motion, we observe a surprising and seemingly counter-intuitive resilience against attacks performed on relatively "static" video chunks, which turn out to contain hard-to-imitate involuntary movements. We show that the accuracy of Vamos on the task of verifying whole length videos exceeds 93% against the new attacks.
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