New Defense Against Adversarial Attacks On Vision-Language Models
Researchers have developed a new method called 'High-Noise Drift Gating' to improve test-time adversarial defenses for vision-language models. This approach aims to address the vulnerability of models like CLIP to adversarial attacks.
Topics
Developing
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Sources · 7 independent
Modernity/arxiv
“Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models. Authors: Hashmat Shadab Malik, Muzammal Naseer, Salman Khan Abstract: Vision-language models (VLMs) such as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attac...”
Modernity/arxiv
“as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attac”
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