Video Forgery Detection for Surveillance Cameras: A Review

2025-05-04

AI Summary

To address the growing threat of video tampering in surveillance footage—which undermines its admissibility as judicial evidence—this paper presents a systematic survey of video forgery detection techniques tailored to security monitoring scenarios. We propose the first robustness evaluation framework specifically designed for real-world surveillance conditions, characterized by low resolution, high compression, and dynamic illumination variations. The framework integrates compression artifact analysis, temporal consistency verification, and hybrid feature extraction combining deep learning models (CNNs and LSTMs) with handcrafted features. For the first time, we conduct a comprehensive comparative analysis of three mainstream approaches—compression-based feature analysis, frame duplication detection, and machine learning–based methods—elucidating their respective applicability boundaries and performance limitations under practical surveillance constraints. Our empirical study identifies characteristic failure modes of existing detectors across typical surveillance conditions, thereby providing evidence-based guidance for forensic system design, algorithm optimization, and standardization efforts in digital video authentication.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Detect video forgery in surveillance camera footage
• Review forensic techniques for verifying video authenticity
• Address evolving forgery methods with robust detection

Innovation

• Compression-based analysis for forgery detection
• Frame duplication detection techniques
• Machine learning-based forensic approaches