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Demo: Security of Deep Learning based Automated Lane Centering under Physical-World Attack.

, , , , , and . SP (Workshops), page 244. IEEE, (2021)

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Poster: On the System-Level Effectiveness of Physical Object-Hiding Adversarial Attack in Autonomous Driving., , , , and . CCS, page 3479-3481. ACM, (2022)Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack., , , , and . ICCV, page 4389-4400. IEEE, (2023)ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving., , , , , and . CoRR, (2024)Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack., , , , , and . CoRR, (2020)Interpretable Deep Learning under Fire., , , , , and . USENIX Security Symposium, page 1659-1676. USENIX Association, (2020)Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack., , , , , and . USENIX Security Symposium, page 3309-3326. USENIX Association, (2021)Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks., , , , , , , , and . SP, page 176-194. IEEE, (2021)Integration of Static and Dynamic Code Stylometry Analysis for Programmer De-anonymization., , and . AISec@CCS, page 74-84. ACM, (2018)Rendered Private: Making GLSL Execution Uniform to Prevent WebGL-based Browser Fingerprinting., , , and . USENIX Security Symposium, page 1645-1660. USENIX Association, (2019)SlowTrack: Increasing the Latency of Camera-Based Perception in Autonomous Driving Using Adversarial Examples., , , and . AAAI, page 4062-4070. AAAI Press, (2024)