6 citations · 26 across the 10 of their papers we have counts for
15 papers
Typographic Attacks in a Multi-Image Setting
Xiaomeng Wang, Zhengyu Zhao, Martha Larson
Large Vision-Language Models (LVLMs) are susceptible to typographic attacks, which are misclassifications caused by an attack text that is added to an image. In this paper, we intr…
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Junhao Zheng, Chenhao Lin, Jiahao Sun +3
Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against…
Adversarial Example Soups: Improving Transferability and Stealthiness for Free
Bo Yang, Hengwei Zhang, Jindong Wang +4
Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing…
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +2
Adversarial training has achieved substantial performance in defending image retrieval against adversarial examples. However, existing studies in deep metric learning (DML) still s…
Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Models
Yugeng Liu, Tianshuo Cong, Zhengyu Zhao +3
Large Language Models (LLMs) undergo continuous updates to improve user experience. However, prior research on the security and safety implications of LLMs has primarily focused on…
Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis
Yihan Ma, Zhengyu Zhao, Xinlei He +3
Large text-to-image models have shown remarkable performance in synthesizing high-quality images. In particular, the subject-driven model makes it possible to personalize the image…