Image and Video Security: Deepfake Detection, Provenance, and Robust Tamper Forensics

This Research Topic explores recent advances in image and video security, with a focus on deepfake detection, content provenance, and robust tamper forensics. The rapid evolution of generative models and editing tools has made it increasingly difficult to distinguish authentic visual content from manipulated media. While these technologies enable creative and practical applications across industries, they also introduce significant risks related to misinformation, fraud, and the erosion of trust in digital content.Original research, reviews, case studies and FAIR=2 Dataset Articles addressing methods for detecting, tracing, and verifying the integrity of visual data are welcome. Submissions may cover developments in deepfake detection algorithms, multimodal forensic analysis, cryptographic provenance frameworks, and watermarking techniques for content authentication. Contributions may also explore adversarial robustness, dataset biases, and explainability in AI-based forensic systems. This Research Topic aims to advance reliable and transparent solutions for identifying and mitigating manipulation in image and video data.To gather further insights in this evolving field, articles are invited — but not limited to — the following themes:-- Deepfake detection using machine learning and multimodal approaches-- Techniques for establishing and verifying content provenance and authenticity-- Digital watermarking and fingerprinting for media traceability-- Forensic analysis of image and video tampering, including compression and editing artifacts-- Robustness of detection systems against adversarial attacks and synthetic media evolution-- Benchmark datasets, evaluation protocols, and standardization efforts in media forensics-- Ethical, legal, and societal implications of synthetic media and misinformationBy addressing both technical innovations and broader societal challenges, this collection seeks to provide a comprehensive perspective on securing visual media and strengthening trust in digital information ecosystems. Topic editors: Guipeng Lan, Dr. ASHA S, Nirmal Kaur Source: https://www.frontiersin.org/research-topics/79908/image-and-video-security-deepfake-detection-provenance-and-robust-tamper-forensics