  In Autonomous Driving (AD), real-time perception is a critical component
responsible for detecting surrounding objects to ensure safe driving. While
researchers have extensively explored the integrity of AD perception due to its
safety and security implications, the aspect of availability (real-time
performance) or latency has received limited attention. Existing works on
latency-based attack have focused mainly on object detection, i.e., a component
in camera-based AD perception, overlooking the entire camera-based AD
perception, which hinders them to achieve effective system-level effects, such
as vehicle crashes. In this paper, we propose SlowTrack, a novel framework for
generating adversarial attacks to increase the execution time of camera-based
AD perception. We propose a novel two-stage attack strategy along with the
three new loss function designs. Our evaluation is conducted on four popular
camera-based AD perception pipelines, and the results demonstrate that
SlowTrack significantly outperforms existing latency-based attacks while
maintaining comparable imperceptibility levels. Furthermore, we perform the
evaluation on Baidu Apollo, an industry-grade full-stack AD system, and LGSVL,
a production-grade AD simulator, with two scenarios to compare the system-level
effects of SlowTrack and existing attacks. Our evaluation results show that the
system-level effects can be significantly improved, i.e., the vehicle crash
rate of SlowTrack is around 95% on average while existing works only have
around 30%.

Computer Vision and Pattern Recognition;Cryptography and Security