Image and Video Forgery Detection

The rapid advancement of digital media editing tools and generative artificial intelligence has significantly increased the prevalence of manipulated images and videos. From traditional copy–move forgeries to sophisticated deepfake synthesis, digital content manipulation threatens information integrity, cybersecurity, journalism, and legal systems. This review provides a comprehensive analysis of digital image and video forgery detection techniques, benchmark datasets, evaluation metrics, and emerging research challenges. We categorize detection methods into classical forensic approaches and deep learning-based frameworks, examining their performance across detection accuracy, robustness, computational efficiency, interpretability, and multimodal integration. We further analyze major public datasets, highlight domain generalization challenges, and discuss adversarial vulnerabilities. Finally, we outline future research directions including cross-domain learning, physics-informed modeling, lightweight edge architectures, explainable AI, and self-supervised multimodal learning. This review serves as a unified technical reference for researchers, practitioners, and policymakers working in digital media forensics.