AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
This addresses the problem of improving deepfake localization for security and media integrity, though it is incremental as it focuses on dataset creation rather than a new detection method.
The researchers tackled the challenge of detecting and localizing realistic deepfake audio-visual content by creating the AV-Deepfake1M dataset, which includes over 1 million videos with various manipulations, and benchmark tests showed a significant performance drop in existing methods compared to previous datasets.
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 .