Multi-Shield rejects adversarial images by abstaining whenever a standard image classifier and a CLIP zero-shot classifier disagree, which raises robust accuracy noticeably under ordinary attacks and modestly under adaptive attacks.
Deep neural rejection against adver- sarial examples,
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Robust image classification with multi-modal large language models
Multi-Shield rejects adversarial images by abstaining whenever a standard image classifier and a CLIP zero-shot classifier disagree, which raises robust accuracy noticeably under ordinary attacks and modestly under adaptive attacks.