Zizhi Jin
Papers
2
Total Citations
56
H-Index
2
About
Zizhi Jin is a leading researcher in the security of autonomous systems, specializing in the vulnerabilities of LiDAR-based perception. His work exposes critical physical-layer threats to autonomous vehicles and robots, demonstrating how adversaries can manipulate 3D object detection through laser-based spoofing attacks. His most influential paper, "PLA-LiDAR: Physical Laser Attacks against LiDAR-based 3D Object Detection in Autonomous Vehicle" (2023), with 51 citations, pioneered the concept of injecting fake point clouds to deceive obstacle detection systems, directly challenging the safety assumptions of modern autonomous driving. Jin further quantified these risks in "Laser-Based LiDAR Spoofing: Effects Validation, Capability Quantification, and Countermeasures" (2024), providing a comprehensive framework for assessing attack feasibility and proposing robust defensive strategies. His work has been instrumental in bridging the gap between theoretical cybersecurity and real-world autonomous vehicle safety, earning recognition for its practical implications. By systematically exposing how physical laser attacks can compromise LiDAR sensors, Jin has established himself as a key voice in the emerging field of adversarial perception, influencing both academic research and industry safety standards.
Research Focus
Key Achievements
Top Papers
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- 2