Citation

Jiang W, Li H, Liu S, Luo X, Lu R. IEEE Trans. Vehicular Tech. 2020; 69(4): 4439-4449.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TVT.2020.2977378

PMID

unavailable

Abstract

With the ongoing development and improvement of deep learning technology, autonomous vehicles (AVs) have made tremendous progress in recent years. Despite its great potential, AV supported by deep learning technology still faces numerous security threats, which prevent AV from being putting into large-scale practice. Aiming at this challenging situation, in this paper, we would like to exploit two attacks against deep learning algorithms in traffic sign recognition system by leveraging particle swarm optimization. Specifically, we first exploit the PAPSO (poisoning attack with particle swarm optimization) which focuses on the training process of the deep learning algorithms in the traffic sign recognition system, i.e., the attacker injects crafted samples into the training dataset, causing a reduction in classification accuracy of the traffic sign recognition system. Then, we also explore the EAPSO (evasion attack with particle swarm optimization) which on the other hand focuses on the interference process of the deep learning algorithms, i.e., the attacker adds some hardly perceptible perturbations to the targeted test sample, leading to a misclassification on it. Extensive experiments are conducted to shed light on the effectiveness of our attacks, and some corresponding defense strategies are also presented.

Language: en

Keywords

autonomous vehicles; Autonomous vehicles; AV; deep learning; deep learning algorithms; EAPSO; Electronic mail; evasion attack; learning (artificial intelligence); Machine learning; neural nets; PAPSO; particle swarm optimisation; particle swarm optimization; Particle swarm optimization; Perturbation methods; poisoning attack; Security; security of data; security threats; traffic engineering computing; traffic sign recognition; traffic sign recognition system; Training