Protecting Autonomous Cars from Phantom Attacks

Author:

Nassi Ben1,Mirsky Yisroel1,Shams Jacob1,Ben-Netanel Raz1,Nassi Dudi1,Elovici Yuval1

Affiliation:

1. Ben-Gurion University of the Negev, Beersheba, Israel

Abstract

Enabling object detectors to better distinguish between real and fake objects in semi-autonomous and fully autonomous vehicles.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference40 articles.

1. Anker. 2019. Nebula Capsule; https://amzn.to/3XWDrgY. Anker. 2019. Nebula Capsule; https://amzn.to/3XWDrgY.

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3. GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

4. Brown T.B. et al. Adversarial patch (2017); arXiv preprint arXiv:1712.09665. Brown T.B. et al. Adversarial patch (2017); arXiv preprint arXiv:1712.09665.

5. Carlini , N. and Wagner , D . Towards evaluating the robustness of neural networks . In 2017 IEEE Symp. on Security and Privacy, 39--57 . Carlini, N. and Wagner, D. Towards evaluating the robustness of neural networks. In 2017 IEEE Symp. on Security and Privacy, 39--57.

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1. Hardware security of autonomous vehicles;Handbook of Power Electronics in Autonomous and Electric Vehicles;2024

2. Basic Safety Message (BSM) Test Data Generation for Vehicle Security Machine Learning Systems;2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE);2023-07-24

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