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Article overview
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Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack | Takami Sato
; Junjie Shen
; Ningfei Wang
; Yunhan Jack Jia
; Xue Lin
; Qi Alfred Chen
; | Date: |
3 Mar 2020 | Abstract: | Lane-Keeping Assistance System (LKAS) is convenient and widely available
today, but also extremely security and safety critical. In this work, we design
and implement the first systematic approach to attack real-world DNN-based
LKASes. We identify dirty road patches as a novel and domain-specific threat
model for practicality and stealthiness. We formulate the attack as an
optimization problem, and address the challenge from the inter-dependencies
among attacks on consecutive camera frames. We evaluate our approach on a
state-of-the-art LKAS and our preliminary results show that our attack can
successfully cause it to drive off lane boundaries within as short as 1.3
seconds. | Source: | arXiv, 2003.1782 | Services: | Forum | Review | PDF | Favorites |
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