# Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

arXiv: https://arxiv.org/abs/2101.00848 · HTML: https://mlsanity.com/paper/2101.00848/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2021.

## Built on by (most cited first, 8 of 8)

- [Panoramic Video Salient Object Detection with Ambisonic Audio Guidance](https://mlsanity.com/paper/2211.14419/) (2022)
- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information](https://mlsanity.com/paper/2201.09487/) (2022)
- [SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization](https://mlsanity.com/paper/2112.04154/) (2021)
- [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)
- [Video Forgery Detection for Surveillance Cameras: A Review](https://mlsanity.com/paper/2505.03832/) (2025)
- [MultiFormer: A Multi-Person Pose Estimation System Based on CSI and Attention Mechanism](https://mlsanity.com/paper/2505.22555/) (2025)

## Abstract (arXiv)

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.