1M-Deepfakes Detection Challenge
This addresses digital media security by engaging the research community in developing methods for deepfake detection and localization, though it is incremental as it builds on existing datasets and tasks.
The paper introduces the 1M-Deepfakes Detection Challenge to tackle the problem of detecting and localizing deepfake content in videos, using the AV-Deepfake1M dataset with over 1 million manipulated videos across 2,000 subjects.
The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M.