Deepfake Media Forensics: State of the Art and Challenges Ahead

• URL: http://arxiv.org/abs/2408.00388v2
• Date: Tue, 13 Aug 2024 15:10:20 GMT
• Title: Deepfake Media Forensics: State of the Art and Challenges Ahead
• Authors: Irene Amerini, Mauro Barni, Sebastiano Battiato, Paolo Bestagini, Giulia Boato, Tania Sari Bonaventura, Vittoria Bruni, Roberto Caldelli, Francesco De Natale, Rocco De Nicola, Luca Guarnera, Sara Mandelli, Gian Luca Marcialis, Marco Micheletto, Andrea Montibeller, Giulia Orru', Alessandro Ortis, Pericle Perazzo, Giovanni Puglisi, Davide Salvi, Stefano Tubaro, Claudia Melis Tonti, Massimo Villari, Domenico Vitulano,
• Abstract summary: AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity. Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
• Score: 51.33414186878676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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