Deepfake Media Forensics: State of the Art and Challenges Ahead

TL;DR

This paper reviews the current state and challenges of Deepfake media forensics, focusing on detection techniques, ethical concerns, and future research directions in AI-generated synthetic media.

Contribution

It provides a comprehensive overview of Deepfake detection methods, their limitations, and future challenges in forensic media analysis.

Findings

Deepfake detection relies on identifying subtle artifacts and inconsistencies.

Current algorithms have limitations in realistic scenarios and evolving Deepfake techniques.

Future research needs to address ethical issues and improve detection robustness.

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.