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.
We first present DFA-CON, a contrastive repre- sentation learning framework designed to detect copyright in- fringement in AI-generated art (see Fig
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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.