Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack

03/03/2020
by   Takami Sato, et al.
0

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.

READ FULL TEXT

page 2

page 3

research
09/14/2020

Hold Tight and Never Let Go: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Automated Lane Centering (ALC) systems are convenient and widely deploye...
research
03/01/2021

Model-Agnostic Defense for Lane Detection against Adversarial Attack

Susceptibility of neural networks to adversarial attack prompts serious ...
research
07/26/2023

Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection

Localization in high-level Autonomous Driving (AD) systems is highly sec...
research
03/02/2022

Clean-Annotation Backdoor Attack against Lane Detection Systems in the Wild

We present the first backdoor attack against the lane detection systems ...
research
05/09/2019

Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain

Deep learning models are known to solve classification and regression pr...
research
06/30/2021

Bio-Inspired Adversarial Attack Against Deep Neural Networks

The paper develops a new adversarial attack against deep neural networks...

Please sign up or login with your details

Forgot password? Click here to reset