# Video Forgery Detection for Surveillance Cameras: A Review

arXiv: https://arxiv.org/abs/2505.03832 · HTML: https://mlsanity.com/paper/2505.03832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 118 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 7 of 7)

- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [Autoencoder with recurrent neural networks for video forgery detection](https://mlsanity.com/paper/1708.08754/) (2017)
- [Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos](https://mlsanity.com/paper/2101.00848/) (2021)
- [Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information](https://mlsanity.com/paper/2201.09487/) (2022)
- [A novel Facial Recognition technique with Focusing on Masked Faces](https://mlsanity.com/paper/2501.04444/) (2025)
- [Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness](https://mlsanity.com/paper/2207.03409/) (2022)
- [SurFi: Detecting Surveillance Camera Looping Attacks with Wi-Fi Channel State Information (Extended Version)](https://mlsanity.com/paper/1904.01350/) (2019)

## Abstract (arXiv)

The widespread availability of video recording through smartphones and digital devices has made video-based evidence more accessible than ever. Surveillance footage plays a crucial role in security, law enforcement, and judicial processes. However, with the rise of advanced video editing tools, tampering with digital recordings has become increasingly easy, raising concerns about their authenticity. Ensuring the integrity of surveillance videos is essential, as manipulated footage can lead to misinformation and undermine judicial decisions. This paper provides a comprehensive review of existing forensic techniques used to detect video forgery, focusing on their effectiveness in verifying the authenticity of surveillance recordings. Various methods, including compression-based analysis, frame duplication detection, and machine learning-based approaches, are explored. The findings highlight the growing necessity for more robust forensic techniques to counteract evolving forgery methods. Strengthening video forensic capabilities will ensure that surveillance recordings remain credible and admissible as legal evidence.