The Competition of Fairness in AI Face Detection

This competition focuses on advancing fairness-aware detection of AI-generated (deepfake) faces and promoting new methodological innovations, addressing a critical gap where fairness methods developed in machine learning have been largely overlooked in deepfake detection. In the competition, participants will work with two large-scale datasets provided by the organizers: AI-Face (CVPR 2025), a million-scale, demographically annotated dataset for training and validation, and PDID (AAAI 2024), a newly curated dataset comprising real-world deepfake incidents, reserved for testing. Participants are tasked with developing models that achieve strong utility performance (e.g., AUC) while ensuring fairness generalization under real-world deployment conditions. The baseline method, PG-FDD (published at CVPR 2024 from the organizer’s group), which demonstrates state-of-the-art performance in fairness generalization for AI face detection, will be provided to support participation.The competition’s potential impact includes fostering the development of robust, fair, and generalizable deepfake detectors, raising awareness of fairness challenges in combating AI-generated fakes, and promoting responsible AI and machine learning deployment in societal applications such as media forensics and digital identity verification. Our competition is fortunately sponsored by Deep Media AI and Originality.AI companies. The challenge link is https://sites.google.com/view/aifacedetection/home.