arXiv: cs.AI preprintsInternational6 November 2026
UniGADD: Universal Generator-Agnostic Deepfake Detector
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arXiv:2611.07647v1 Announce Type: cross Abstract: In this paper, we introduce Universal Generator-Agnostic Deepfake Detector (UniGADD), a novel method that employs supervised learning to achieve high accuracy on known deepfake generators, while maintaining robust performance on previously unseen ones. The proposed approach follows a two-stage optimisation process. In the first stage, a contrastive loss encourages the model to learn discriminative feature embeddings from real and known fake images, resulting in strong performance within the training domain. In the second stage, the embedding sp
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