Papers by Aoyang Zhou
Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack (2025.findings-naacl)
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| Challenge: | Visual-Language Pre-training (VLP) models are vulnerable to adversarial examples . previous studies have focused on improving adversariality of models . |
| Approach: | They propose a local shuffle and sample-based attack that randomly shufts one of the local image blocks and generates adversarial images and samples around them. |
| Outcome: | The proposed attack outperforms other advanced attacks on Large Vision-Language Models and outperformed previous attacks on Visual-Langue Pre-training models. |