3 citations · 16 across the 42 of their papers we have counts for
19 papers · 1 filter
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…
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…
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…
Reading Between the Pixels: An Inscriptive Jailbreak Attack on Text-to-Image Models
Zonghao Ying, Haowen Dai, Lianyu Hu +5
Modern text-to-image (T2I) models can now render legible, paragraph-length text, enabling a fundamentally new class of misuse. We identify and formalize the inscriptive jailbreak,…
SPARK: Jailbreaking T2V Models by Synergistically Prompting Auditory and Recontextualized Knowledge
Zonghao Ying, Moyang Chen, Nizhang Li +6
Jailbreak attacks can circumvent model safety guardrails and reveal critical blind spots. Prior attacks on text-to-video (T2V) models typically add adversarial perturbations to obv…
Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
Jiangfan Liu, Yongkang Guo, Fangzhi Zhong +7
The generation of safety-critical scenarios in simulation has become increasingly crucial for safety evaluation in autonomous vehicles prior to road deployment in society. However,…
Bench2ADVLM: A Closed-Loop Benchmark for Vision-language Models in Autonomous Driving
Tianyuan Zhang, Ting Jin, Lu Wang +5
Vision-Language Models (VLMs) have recently emerged as a promising paradigm in autonomous driving (AD). However, current performance evaluation protocols for VLM-based AD systems (…
PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation
Zonglei Jing, Xiao Yang, Xiaoqian Li +4
Text-to-image (T2I) models have demonstrated remarkable generative capabilities but remain vulnerable to producing not-safe-for-work (NSFW) content, such as violent or explicit ima…
Manipulating Multimodal Agents via Cross-Modal Prompt Injection
Le Wang, Zonghao Ying, Tianyuan Zhang +5
The emergence of multimodal large language models has redefined the agent paradigm by integrating language and vision modalities with external data sources, enabling agents to bett…
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving
Lu Wang, Tianyuan Zhang, Yang Qu +5
Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities; however, these models remain highly susceptible to adversaria…
CogMorph: Cognitive Morphing Attacks for Text-to-Image Models
Zonglei Jing, Zonghao Ying, Le Wang +4
The development of text-to-image (T2I) generative models, that enable the creation of high-quality synthetic images from textual prompts, has opened new frontiers in creative desig…
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…
Module-wise Adaptive Adversarial Training for End-to-end Autonomous Driving
Tianyuan Zhang, Lu Wang, Jiaqi Kang +5
Recent advances in deep learning have markedly improved autonomous driving (AD) models, particularly end-to-end systems that integrate perception, prediction, and planning stages,…
LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions
Tianyuan Zhang, Lu Wang, Hainan Li +5
Lane detection (LD) is an essential component of autonomous driving systems, providing fundamental functionalities like adaptive cruise control and automated lane centering. Existi…
Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt
Zonghao Ying, Aishan Liu, Tianyuan Zhang +4
In the realm of large vision language models (LVLMs), jailbreak attacks serve as a red-teaming approach to bypass guardrails and uncover safety implications. Existing jailbreaks pr…
Towards Robust Physical-world Backdoor Attacks on Lane Detection
Xinwei Zhang, Aishan Liu, Tianyuan Zhang +2
Deep learning-based lane detection (LD) plays a critical role in autonomous driving systems, such as adaptive cruise control. However, it is vulnerable to backdoor attacks. Existin…
GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing
Yisong Xiao, Aishan Liu, QianJia Cheng +6
Large Vision-Language Models (LVLMs) have been widely adopted in various applications; however, they exhibit significant gender biases. Existing benchmarks primarily evaluate gende…
Pre-trained Trojan Attacks for Visual Recognition
Aishan Liu, Xinwei Zhang, Yisong Xiao +6
Pre-trained vision models (PVMs) have become a dominant component due to their exceptional performance when fine-tuned for downstream tasks. However, the presence of backdoors with…
Adversarial Examples in the Physical World: A Survey
Jiakai Wang, Xianglong Liu, Jin Hu +6
Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples, raising broad security concerns about their applications. Besides the attacks in the digit…