Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection
arXiv:2610.21974v1 Announce Type: cross Abstract: Deepfake detectors safeguard against misinformation, rendering their failures as much a societal as a technical concern. As generators constantly evolve, detectors trained on static datasets miss newly arising threats, while progressive training interferes catastrophically. Yet, mitigating forgetfulness in terms of accuracy, the prevalent focus of continual learning (CL), is insufficient to reflect real-world impact. We therefore scrutinize the…