mlsanityData to 7 Oct 2026

Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook

arXiv 2411.19537 ↗First posted 29 Nov 2024cs.CV cs.AI cs.LGcited by 10

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Abstract (arXiv)

We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and construct taxonomies of deepfake generation and detection methods, illustrating the important groups of methods. Next, we gather datasets used for deepfake detection and provide updated rankings of the best performing detectors on the most popular datasets. In addition, we develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content. The results indicate that state-of-the-art detectors fail to generalize to deepfakes generated by unseen generators. Our project page and new benchmark are available at https://github.com/CroitoruAlin/biodeep.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 277 of its 391 references that are in the map. First posted on arXiv 29 Nov 2024.

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277 of 391 references.

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Citing papers per year: 2024: 1; 2025: 4; 2026: 5.

10 papers.