Deepfake Media Forensics: State of the Art and Challenges Ahead

TL;DR

This paper surveys the current landscape of deepfake forensics, covering detection, attribution/recognition, passive and active authentication, and performance in realistic scenarios. It synthesizes core techniques such as artifact/texture analysis, multimodal detection, model fingerprinting, watermarking, and cryptographic provenance, while highlighting persistent challenges like generalization to new generative methods, compression effects, adversarial manipulation, and explainability. The authors discuss practical countermeasures, including continual learning and MLOps, edge/cloud trusted processing, and proactive authentication via watermarks and signatures, to enable robust, scalable deployment in real-world media ecosystems. Overall, the work emphasizes a need for standardized datasets, resilient methodologies, and integrated security practices to mitigate emerging deepfake threats across diverse applications.

Abstract

AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.