DFA-CON trains a ResNet-50 with supervised contrastive loss to embed originals and their AI-forged versions close together, achieving the best reported F1 on the DeepfakeArt benchmark among the tested models.
A similarity threshold is first determined using validation set and then applied during test- ing to make binary decisions
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DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art
DFA-CON trains a ResNet-50 with supervised contrastive loss to embed originals and their AI-forged versions close together, achieving the best reported F1 on the DeepfakeArt benchmark among the tested models.