Model-Agnostic Defense for Lane Detection against Adversarial Attack
This addresses safety concerns for autonomous driving systems by providing a defense against adversarial attacks on lane detection, though it is incremental as it builds on existing adversarial attack research.
The paper tackles the susceptibility of lane detection models to adversarial attacks by proposing a modular lane verification system that is model-agnostic. The result shows that with a 10% inference time impact, the system detects 96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98% of patch attacks while maintaining at least 95% true lane identification accuracy.
Susceptibility of neural networks to adversarial attack prompts serious safety concerns for lane detection efforts, a domain where such models have been widely applied. Recent work on adversarial road patches have successfully induced perception of lane lines with arbitrary form, presenting an avenue for rogue control of vehicle behavior. In this paper, we propose a modular lane verification system that can catch such threats before the autonomous driving system is misled while remaining agnostic to the particular lane detection model. Our experiments show that implementing the system with a simple convolutional neural network (CNN) can defend against a wide gamut of attacks on lane detection models. With a 10% impact to inference time, we can detect 96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98% of patch attacks while preserving accurate identification at least 95% of true lanes, indicating that our proposed verification system is effective at mitigating lane detection security risks with minimal overhead.