论文标题
自我监管的多模式视频伪造攻击检测
Self-supervised Multi-Modal Video Forgery Attack Detection
论文作者
论文摘要
视频伪造攻击通过用不现实的综合捕获视频捕获来威胁监视系统，这可以由最新的增强现实和虚拟现实技术提供动力。从机器的感知方面，视觉对象通常具有在录制过程中自然同步的RF签名。与视频捕获相反，鉴于其隐藏和无处不在的性质，RF签名更难攻击。在这项工作中，我们使用视觉和无线方式研究了多模式视频伪造攻击检测方法。由于基于无线信号的人类感知对环境敏感，因此我们提出了一种自制的训练策略，以使系统能够在没有外部注释的情况下工作，因此可以适应不同的环境。我们的方法达到了完美的人类检测准确性和高伪造攻击检测精度为94.38％，这与监督方法相当。
Video forgery attack threatens the surveillance system by replacing the video captures with unrealistic synthesis, which can be powered by the latest augment reality and virtual reality technologies. From the machine perception aspect, visual objects often have RF signatures that are naturally synchronized with them during recording. In contrast to video captures, the RF signatures are more difficult to attack given their concealed and ubiquitous nature. In this work, we investigate multimodal video forgery attack detection methods using both vision and wireless modalities. Since wireless signal-based human perception is environmentally sensitive, we propose a self-supervised training strategy to enable the system to work without external annotation and thus can adapt to different environments. Our method achieves a perfect human detection accuracy and a high forgery attack detection accuracy of 94.38% which is comparable with supervised methods.
