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.

View More Papers

Experimental Analyses of the Physical Surveillance Risks in Client-Side...

Ashish Hooda (University of Wisconsin-Madison), Andrey Labunets (UC San Diego), Tadayoshi Kohno (University of Washington), Earlence Fernandes (UC San Diego)

Read More

NODLINK: An Online System for Fine-Grained APT Attack Detection...

Shaofei Li (Key Laboratory of High-Confidence Software Technologies (MOE), School of Computer Science, Peking University), Feng Dong (Huazhong University of Science and Technology), Xusheng Xiao (Arizona State University), Haoyu Wang (Huazhong University of Science and Technology), Fei Shao (Case Western Reserve University), Jiedong Chen (Sangfor Technologies Inc.), Yao Guo (Key Laboratory of High-Confidence Software Technologies…

Read More

Enhanced Vehicular Roll-Jam Attack using a Known Noise Source

Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

Read More

TrustSketch: Trustworthy Sketch-based Telemetry on Cloud Hosts

Zhuo Cheng (Carnegie Mellon University), Maria Apostolaki (Princeton University), Zaoxing Liu (University of Maryland), Vyas Sekar (Carnegie Mellon University)

Read More