# Self-supervised Multi-Modal Video Forgery Attack Detection

arXiv: https://arxiv.org/abs/2209.06345 · HTML: https://mlsanity.com/paper/2209.06345/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Panoramic Video Salient Object Detection with Ambisonic Audio Guidance](https://mlsanity.com/paper/2211.14419/) (2022)
- [Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study](https://mlsanity.com/paper/2308.02412/) (2023)
- [Recovering Surveillance Video Using RF Cues](https://mlsanity.com/paper/2212.13340/) (2022)
- [Object Segmentation with Audio Context](https://mlsanity.com/paper/2301.10295/) (2023)

## Abstract (arXiv)

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.