Zheng, Yixing (Heriot Watt University), Xiao, Yizhuo (Heriot-Watt University), Zhu, Zhongpan (Tongji University), Erden, Mustafa Suphi (Heriot-Watt University), Wang, Cheng (Heriot-Watt University)

CADiffusion: Controllable Adversarial Diffusion for Attacking Lane Detection of Autonomous Vehicles

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

Adversarial example generation is crucial for accelerating the testing of autonomous vehicles (AVs). With the introduction of end-to-end AVs, adversarial images are becoming more important than adversarial behavior attacks, as perception and decision-making no longer have explicit information transmission. Generative AI-based methods show promising results when generating new images. However, they are usually hardly controllable and struggle with semantic consistency in generated images. In this paper, we focus on lane detection as an example and integrate a control network along with a bootstrapped language-image pretraining model into the stable diffusion framework to enhance the controllability and realism of generated adversarial examples. Leveraging the PBLMD dataset, we successfully generate diverse adversarial examples such as occluded and worn lane lines. Experimental results demonstrate that these examples reduce the lane detection performance of YOLOP by an average of 11% while maintaining high fidelity, demonstrating the approach's efficacy.