https://github.com/ASGuard-UCI/MSF-ADV

MSF-ADV is a novel physical-world adversarial attack method, which can fool the Multi Sensor Fusion (MSF) based autonomous driving (AD) perception in the victim autonomous vehicle (AV) to fail in detecting a front obstacle and thus crash into it. This work is accepted by IEEE S&P 2021.
https://github.com/ASGuard-UCI/MSF-ADV

Last synced: 3 months ago
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MSF-ADV is a novel physical-world adversarial attack method, which can fool the Multi Sensor Fusion (MSF) based autonomous driving (AD) perception in the victim autonomous vehicle (AV) to fail in detecting a front obstacle and thus crash into it. This work is accepted by IEEE S&P 2021.

• Host: GitHub
• URL: https://github.com/ASGuard-UCI/MSF-ADV
• Owner: ASGuard-UCI
• License: mit
• Created: 2021-07-28T19:17:10.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-08-04T07:06:35.000Z (over 3 years ago)
• Last Synced: 2024-08-02T01:24:22.464Z (7 months ago)
• Language: Python
• Homepage: https://sites.google.com/view/cav-sec/msf-adv
• Size: 11.3 MB
• Stars: 72
• Watchers: 1
• Forks: 14
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-yolo-object-detection - ASGuard-UCI/MSF-ADV - UCI/MSF-ADV?style=social"/> : MSF-ADV is a novel physical-world adversarial attack method, which can fool the Multi Sensor Fusion (MSF) based autonomous driving (AD) perception in the victim autonomous vehicle (AV) to fail in detecting a front obstacle and thus crash into it. "Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks". (**[IEEE S&P 2021](https://www.computer.org/csdl/proceedings-article/sp/2021/893400b302/1t0x9btzenu)**) (Applications)
• awesome-yolo-object-detection - ASGuard-UCI/MSF-ADV - UCI/MSF-ADV?style=social"/> : MSF-ADV is a novel physical-world adversarial attack method, which can fool the Multi Sensor Fusion (MSF) based autonomous driving (AD) perception in the victim autonomous vehicle (AV) to fail in detecting a front obstacle and thus crash into it. "Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks". (**[IEEE S&P 2021](https://www.computer.org/csdl/proceedings-article/sp/2021/893400b302/1t0x9btzenu)**) (Applications)