Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey

• URL: http://arxiv.org/abs/2406.06965v3
• Date: Wed, 14 Aug 2024 15:38:49 GMT
• Title: Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey
• Authors: Ping Liu, Qiqi Tao, Joey Tianyi Zhou,
• Abstract summary: As AI-generated media become more realistic, the risk of misuse to spread misinformation and commit identity fraud increases. This work traces the evolution from traditional single-modality methods to sophisticated multi-modal approaches that handle audio-visual and text-visual scenarios. To our knowledge, this is the first survey of its kind.
• Score: 40.11614155244292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This survey addresses the critical challenge of deepfake detection amidst the rapid advancements in artificial intelligence. As AI-generated media, including video, audio and text, become more realistic, the risk of misuse to spread misinformation and commit identity fraud increases. Focused on face-centric deepfakes, this work traces the evolution from traditional single-modality methods to sophisticated multi-modal approaches that handle audio-visual and text-visual scenarios. We provide comprehensive taxonomies of detection techniques, discuss the evolution of generative methods from auto-encoders and GANs to diffusion models, and categorize these technologies by their unique attributes. To our knowledge, this is the first survey of its kind. We also explore the challenges of adapting detection methods to new generative models and enhancing the reliability and robustness of deepfake detectors, proposing directions for future research. This survey offers a detailed roadmap for researchers, supporting the development of technologies to counter the deceptive use of AI in media creation, particularly facial forgery. A curated list of all related papers can be found at \href{https://github.com/qiqitao77/Comprehensive-Advances-in-Deepfake-Detection-Spanning-Diverse-Modalitie s}{https://github.com/qiqitao77/Awesome-Comprehensive-Deepfake-Detection}.

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