Video Forgery Detection for Surveillance Cameras: A Review

TL;DR

This review paper examines current forensic techniques for detecting video forgery in surveillance footage, emphasizing the need for more robust methods to maintain the credibility of digital evidence amid advancing tampering tools.

Contribution

It provides a comprehensive overview of existing forensic methods, analyzing their effectiveness and highlighting gaps in current video forgery detection techniques.

Findings

Detection methods vary in effectiveness

Machine learning approaches show promise

Need for more robust forensic techniques

Abstract

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.