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25 April 2024 |
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Hold Tight and Never Let Go: Security of Deep Learning based Automated Lane Centering under Physical-World Attack | | Takami Sato
; Junjie Shen
; Ningfei Wang
; Yunhan Jack Jia
; Xue Lin
; Qi Alfred Chen
; | | Date: |
14 Sep 2020 | | Abstract: | Automated Lane Centering (ALC) systems are convenient and widely deployed
today, but also highly security and safety critical. In this work, we are the
first to systematically study the security of state-of-the-art deep learning
based ALC systems in their designed operational domains under physical-world
adversarial attacks. We formulate the problem with a safety-critical attack
goal, and a novel and domain-specific attack vector: dirty road patches. To
systematically generate the attack, we adopt an optimization-based approach and
overcome domain-specific design challenges such as camera frame
inter-dependencies due to dynamic vehicle actuation, and the lack of objective
function design for lane detection models.
We evaluate our attack method on a production ALC system using 80 attack
scenarios from real-world driving traces. The results show that our attack is
highly effective with over 92% success rates and less than 0.95 sec average
success time, which is substantially lower than the average driver reaction
time. Such high attack effectiveness is also found (1) robust to motion model
inaccuracies, different lane detection model designs, and physical-world
factors, and (2) stealthy from the driver’s view. To concretely understand the
end-to-end safety consequences, we further evaluate on concrete real-world
attack scenarios using a production-grade simulator, and find that our attack
can successfully cause the victim to hit the highway concrete barrier or a
truck in the opposite direction with 98% and 100% success rates. We also
discuss defense directions. | | Source: | arXiv, 2009.06701 | | Services: | Forum | Review | PDF | Favorites | |
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