AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset

• URL: http://arxiv.org/abs/2311.15308v2
• Date: Mon, 29 Jul 2024 06:24:07 GMT
• Title: AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
• Authors: Zhixi Cai, Shreya Ghosh, Aman Pankaj Adatia, Munawar Hayat, Abhinav Dhall, Tom Gedeon, Kalin Stefanov,
• Abstract summary: We propose the AV-Deepfake1M dataset for the detection and localization of deepfake audio-visual content. The dataset contains content-driven (i) video manipulations, (ii) audio manipulations, and (iii) audio-visual manipulations for more than 2K subjects resulting in a total of more than 1M videos.
• Score: 21.90332221144928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection and localization of highly realistic deepfake audio-visual content are challenging even for the most advanced state-of-the-art methods. While most of the research efforts in this domain are focused on detecting high-quality deepfake images and videos, only a few works address the problem of the localization of small segments of audio-visual manipulations embedded in real videos. In this research, we emulate the process of such content generation and propose the AV-Deepfake1M dataset. The dataset contains content-driven (i) video manipulations, (ii) audio manipulations, and (iii) audio-visual manipulations for more than 2K subjects resulting in a total of more than 1M videos. The paper provides a thorough description of the proposed data generation pipeline accompanied by a rigorous analysis of the quality of the generated data. The comprehensive benchmark of the proposed dataset utilizing state-of-the-art deepfake detection and localization methods indicates a significant drop in performance compared to previous datasets. The proposed dataset will play a vital role in building the next-generation deepfake localization methods. The dataset and associated code are available at https://github.com/ControlNet/AV-Deepfake1M .

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