LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929237342027776 |
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| author | Sato, Takami Hayakawa, Yuki Suzuki, Ryo Shiiki, Yohsuke Yoshioka, Kentaro Chen, Qi Alfred |
| author_facet | Sato, Takami Hayakawa, Yuki Suzuki, Ryo Shiiki, Yohsuke Yoshioka, Kentaro Chen, Qi Alfred |
| contents | LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research finds that one can manipulate the LiDAR point cloud and fool object detectors by firing malicious lasers against LiDAR. However, these efforts face 3 critical research gaps: (1) considering only one specific LiDAR (VLP-16); (2) assuming unvalidated attack capabilities; and (3) evaluating object detectors with limited spoofing capability modeling and setup diversity.
To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets. To facilitate the measurements, we (1) identify spoofer improvements that significantly improve the latest spoofing capability, (2) identify a new object removal attack that overcomes the applicability limitation of the latest method to new-generation LiDARs, and (3) perform novel mathematical modeling for both object injection and removal attacks based on our measurement results. Through this study, we are able to uncover a total of 15 novel findings, including not only completely new ones due to the measurement angle novelty, but also many that can directly challenge the latest understandings in this problem space. We also discuss defenses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_10555 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies Sato, Takami Hayakawa, Yuki Suzuki, Ryo Shiiki, Yohsuke Yoshioka, Kentaro Chen, Qi Alfred Cryptography and Security Computer Vision and Pattern Recognition Machine Learning LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research finds that one can manipulate the LiDAR point cloud and fool object detectors by firing malicious lasers against LiDAR. However, these efforts face 3 critical research gaps: (1) considering only one specific LiDAR (VLP-16); (2) assuming unvalidated attack capabilities; and (3) evaluating object detectors with limited spoofing capability modeling and setup diversity. To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets. To facilitate the measurements, we (1) identify spoofer improvements that significantly improve the latest spoofing capability, (2) identify a new object removal attack that overcomes the applicability limitation of the latest method to new-generation LiDARs, and (3) perform novel mathematical modeling for both object injection and removal attacks based on our measurement results. Through this study, we are able to uncover a total of 15 novel findings, including not only completely new ones due to the measurement angle novelty, but also many that can directly challenge the latest understandings in this problem space. We also discuss defenses. |
| title | LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies |
| topic | Cryptography and Security Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2303.10555 |