Demo #10: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical. Recently, Dirty Road Patch (DRP) attack is proposed as a state-of-the-art adversarial attack against ALC systems. We will show interactive demonstrations for the malicious road patch generation and the serious consequences.

Paper

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Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina (The Johns Hopkins University Applied Physics Laboratory), Neil Zhenqiang Gong (Duke University), Yinzhi Cao (The Johns Hopkins University)

Demo #10: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

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Practical Blind Membership Inference Attack via Differential Comparisons

Obfuscated Access and Search Patterns in Searchable Encryption

(Short) Object Removal Attacks on LiDAR-based 3D Object Detectors

WIP: On Robustness of Lane Detection Models to Physical-World...