Video Forgery Detection for Surveillance Cameras: A Review
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.