most citedOne Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cs.CV2024★ 1 cited

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

Lin Li, Haoyan Guan, Jianing Qiu +1

Large pre-trained Vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples. This work studies the ad…

cs.CV2023

AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation

Lin Li, Jianing Qiu, Michael Spratling

Deep neural networks are vulnerable to adversarial examples. Adversarial training (AT) is an effective defense against adversarial examples. However, AT is prone to overfitting whi…