Demo: Invisible Adversarial Stripes against Traffic Sign Recognition in Autonomous Driving
Camera-based computer vision is crucial for autonomous vehicle perception. We demonstrate GhostStripe [5], an attack system that uses light-emitting diodes and exploits the camera's rolling shutter effect to generate adversarial stripes that are invisible to humans while misleading traffic sign recognition. To maintain stable attack effectiveness, GhostStripe controls the timing of the modulated light emission, adapting to both the camera's framing operation and the movement of the victim vehicle. Evaluated on real testbeds, GhostStripe can stably spoof traffic sign recognition results for up to 97% of frames to a wrong class when the victim vehicle passes the road section.
Authors
- Pengfei Zhou (ORCID: https://orcid.org/0000-0003-1836-1122)
- Dongfang Guo (ORCID: https://orcid.org/0000-0002-7464-0823)
- Rui Tan (ORCID: https://orcid.org/0000-0001-8441-9973)
- Yimin Dai (ORCID: https://orcid.org/0009-0007-1049-3987)
- Xin Lou (ORCID: https://orcid.org/0000-0001-8910-5666)
- Yuting Wu (ORCID: https://orcid.org/0009-0007-1510-6464)
Institutions
- Singapore Institute of Technology (SG)
- University of Pittsburgh (US)
- Nanyang Technological University (SG)
Publication Details
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1145/3666025.3699401
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- National Research Foundation Singapore