Ryunosuke Kobayashi, Kazuki Nomoto, Yuna Tanaka, Go Tsuruoka (Waseda University), Tatsuya Mori (Waseda University/NICT/RIKEN)

—Object detection is a crucial function that detects the position and type of objects from data acquired by sensors. In autonomous driving systems, object detection is performed using data from cameras and LiDAR, and based on the results, the vehicle is controlled to follow the safest route. However, machine learning-based object detection has been reported to have vulnerabilities to adversarial samples. In this study, we propose a new attack method called “Shadow Hack” for LiDAR object detection models. While previous attack methods mainly added perturbed point clouds to LiDAR data, in this research, we introduce a method to generate “Adversarial Shadows” on the LiDAR point cloud. Specifically, the attacker strategically places materials like aluminum leisure mats to reproduce optimized positions and shapes of shadows on the LiDAR point cloud. This technique can potentially mislead LiDAR-based object detection in autonomous vehicles, leading to congestion and accidents due to actions such as braking and avoidance maneuvers. We reproduce the Shadow Hack attack method using simulations and evaluate the success rate of the attack. Furthermore, by revealing the conditions under which the attack succeeds, we aim to propose countermeasures and contribute to enhancing the robustness of autonomous driving systems.

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MASTERKEY: Automated Jailbreaking of Large Language Model Chatbots

Gelei Deng (Nanyang Technological University), Yi Liu (Nanyang Technological University), Yuekang Li (University of New South Wales), Kailong Wang (Huazhong University of Science and Technology), Ying Zhang (Virginia Tech), Zefeng Li (Nanyang Technological University), Haoyu Wang (Huazhong University of Science and Technology), Tianwei Zhang (Nanyang Technological University), Yang Liu (Nanyang Technological University)

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Modeling and Detecting Internet Censorship Events

Elisa Tsai (University of Michigan), Ram Sundara Raman (University of Michigan), Atul Prakash (University of Michigan), Roya Ensafi (University of Michigan)

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Sunil Manandhar (IBM T.J. Watson Research Center), Kapil Singh (IBM T.J. Watson Research Center), Adwait Nadkarni (William & Mary)

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WIP: The Feasibility of High-performance Message Authentication in Automotive...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), Randy Marchany (Virginia Tech), J. Scot Ransbottom (Virginia Tech)

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