  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.
