Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
org/abs/2211.00680
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Introduces the CIFAR Synthetic Evidence Corpus, a multi-family dataset of AI-manipulated documents with source-separated train/test splits for evaluating detectors of AI-generated legal evidence.
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
The ITW-SM dataset and targeted optimization of detector design choices yield a 26.87% average AUC improvement for state-of-the-art AI-generated image detectors under real-world social media conditions.
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