Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024 文章

ArXiv CS.CV2026-05-28NEWSen作者: Nuria Alina Chandra, Hannah Lee, Ryan Murtfeldt, Lin Qiu, Arnab Karmakar, Emmanuel Tanumihardja, Kevin Farhat, Ben Caffee, Changyeon Lee, Jongwook Choi, Sejin Paik, Aerin Kim, Oren Etzioni

摘要

arXiv:2503.02857v5 Announce Type: replace Abstract: In the age of increasingly realistic generative AI, robust deepfake detection is essential for mitigating fraud and disinformation. While many deepfake detectors report high accuracy on academic datasets, we show that these academic benchmarks are out of date and not representative of real-world deepfakes. We introduce Deepfake-Eval-2024, a new deepfake detection benchmark consisting of in-the-wild deepfakes collected from social media and deepfake detection platform users in 2024. Deepfake-Eval-2024 consists of 45 hours of videos, 56.5 hours of audio, and 1,975 images, encompassing the latest manipulation technologies. The benchmark contains diverse media content from 88 different websites in 52 different languages.

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