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

, , , , , и . SP (Workshops), стр. 244. IEEE, (2021)

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Qi Chen

Qi Chen

Qi Chen

Qi Qi

Chen Chen

 

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Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Control., , , , , и . CoRR, (2020)An Initial Investigation of Protocol Customization., , и . FEAST@CCS, стр. 57-64. ACM, (2017)No One In The Middle: Enabling Network Access Control Via Transparent Attribution., , , , , и . AsiaCCS, стр. 651-658. ACM, (2018)Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction., , , , и . CoRR, (2022)On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving., и . CoRR, (2021)Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking., , , , , и . CoRR, (2019)Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack., , , , , и . CoRR, (2020)Poster: Towards Large-Scale Measurement Study on LiDAR Spoofing Attacks against Object Detection., , , , , и . CCS, стр. 3459-3461. ACM, (2022)QoE Inference Without Application Control., , , , и . Internet-QoE@SIGCOMM, стр. 19-24. ACM, (2016)Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack., , , , , и . USENIX Security Symposium, стр. 3309-3326. USENIX Association, (2021)