Zhongyuan Hau, Kenneth Co, Soteris Demetriou, and Emil Lupu (Imperial College London)

Best Short Paper Award Runner-up!

LiDARs play a critical role in Autonomous Vehicles’ (AVs) perception and their safe operations. Recent works have demonstrated that it is possible to spoof LiDAR return signals to elicit fake objects. In this work we demonstrate how the same physical capabilities can be used to mount a new, even more dangerous class of attacks, namely Object Removal Attacks (ORAs). ORAs aim to force 3D object detectors to fail. We leverage the default setting of LiDARs that record a single return signal per direction to perturb point clouds in the region of interest (RoI) of 3D objects. By injecting illegitimate points behind the target object, we effectively shift points away from the target objects’ RoIs. Our initial results using a simple random point selection strategy show that the attack is effective in degrading the performance of commonly used 3D object detection models.

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RandRunner: Distributed Randomness from Trapdoor VDFs with Strong Uniqueness

Philipp Schindler (SBA Research), Aljosha Judmayer (SBA Research), Markus Hittmeir (SBA Research), Nicholas Stifter (SBA Research, TU Wien), Edgar Weippl (Universität Wien)

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Demo #2: Policy-based Discovery and Patching of Logic Bugs...

Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University) and Dongyan Xu (Purdue University)

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Raising Trust in the Food Supply Chain

Alexander Krumpholz, Marthie Grobler, Raj Gaire, Claire Mason, Shanae Burns (CSIRO Data61)

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Evading Voltage-Based Intrusion Detection on Automotive CAN

Rohit Bhatia (Purdue University), Vireshwar Kumar (Indian Institute of Technology Delhi), Khaled Serag (Purdue University), Z. Berkay Celik (Purdue University), Mathias Payer (EPFL), Dongyan Xu (Purdue University)

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