6 papers
Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models
Shunyuan Bai, Yuchen Ren, Le Yang +3
With the advancement in language modeling, research on adversarial images has recently shifted from image classifiers to vision-language models (VLMs). Despite the new, text-prompt…
On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +2
Vision-Language Pre-training Models (VLPMs) are known to be vulnerable to adversarial attacks. Recent transferable attacks on VLPMs have followed a common pipeline with complicated…
Use as Many Surrogates as You Want: Selective Ensemble Attack to Unleash Transferability without Sacrificing Resource Efficiency
Bo Yang, Hengwei Zhang, Jindong Wang +4
In surrogate ensemble attacks, using more surrogate models yields higher transferability but lower resource efficiency. This practical trade-off between transferability and efficie…
Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +4
Vision Transformers (ViTs) have been widely applied in various computer vision and vision-language tasks. To gain insights into their robustness in practical scenarios, transferabl…
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +4
Transferable adversarial examples are known to cause threats in practical, black-box attack scenarios. A notable approach to improving transferability is using integrated gradients…
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
Runlin Lei, Yuwei Hu, Yuchen Ren +1
Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nod…