Towards Autonomous Driving Model Resistant to Adversarial Attack

Shibly, Kabid Hassan and Hossain, Md Delwar and Inoue, Hiroyuki and Taenaka, Yuzo and Kadobayashi, Youki (2023) Towards Autonomous Driving Model Resistant to Adversarial Attack. Applied Artificial Intelligence, 37 (1). ISSN 0883-9514

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Abstract

Connected and Autonomous Vehicles (CAVs) offer improved efficiency and convenience through innovative embedded devices. However, the development of these technologies has often neglected security measures, leading to vulnerabilities that can be exploited by hackers. Conceding that a CAV system is compromised, it can result in unsafe driving conditions and pose a threat to human safety. Prioritizing both security measures and functional enhancements on development of CAVs is essential to ensure their safety and reliability and enhance consumer trust in the technology. CAVs use artificial intelligence to control their driving behavior, which can be easily influenced by small changes in the model that can significantly impact and potentially mislead the system. To address this issue, this study proposed a defense mechanism that uses an autoencoder and a compressive memory module to store normal image features and prevent unexpected generalization on adversarial inputs. The proposed solution was studied against Hijacking, Vanishing, Fabrication, and Mislabeling attacks using FGSM and AdvGAN against the Nvidia Dave-2 driving model, and was found to be effective, with success rates of 93.8%
and 91.2%
in a Whitebox setup, and 74.1%
and 64.4%
in a Blackbox setup for FGSM and AdvGAN, respectively. That improves the results by 24.7%
in Whitebox setup 21.5%
in Blackbox setup.

Item Type: Article
Subjects: AP Academic Press > Computer Science
Depositing User: Unnamed user with email support@apacademicpress.com
Date Deposited: 12 Jun 2023 04:48
Last Modified: 21 Oct 2024 03:52
URI: http://info.openarchivespress.com/id/eprint/1511

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