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From the 1 of 36 linked papers with an AI index.

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20242026
most citedVisual Adversarial Attack on Vision-Language Models for Autonomous Driving

1 citations · 1 across the 6 of their papers we have counts for

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22 papers · 1 filter

cs.CV2026

SafeGen: Goal-Conditioned Video Diffusion of Safety-Critical Scenarios for VLM-Based Autonomous Driving

Jiangfan Liu, Zexuan Cui, Tianyuan Zhang +7

VLMs are increasingly deployed in AD systems, creating an urgent need for rigorous safety evaluation under rare yet safety-critical scenarios. Among these, interactions with vulner…

cs.CV2026

Technical Report on the CVPR 2026@AdvML Workshop Challenge

Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47

The paper reports on the CVPR 2026@AdvML Workshop Challenge, which evaluated adversarial attacks on multimodal vision‑language agents for autonomous driving using multi‑view visual…

cs.CV2026

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective

Tianyuan Zhang, Xianglong Liu, Aishan Liu +6

Environmental illusions (eg., shadows, reflections, and tire marks) are naturally existing yet overlooked phenomena in real-world driving environments. They can disturb visual perc…

cs.CV20261 cited

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving

Tianyuan Zhang, Lu Wang, Xinwei Zhang +7

Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain highly vulnerable to adversarial…

cs.CV2025

SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs

Shuhan Xu, Siyuan Liang, Hongling Zheng +6

Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject…

cs.CV2025

Universal Camouflage Attack on Vision-Language Models for Autonomous Driving

Dehong Kong, Sifan Yu, Siyuan Liang +4

Visual language modeling for automated driving is emerging as a promising research direction with substantial improvements in multimodal reasoning capabilities. Despite its advance…