Christopher DiPalma, Ningfei Wang, Takami Sato, and Qi Alfred Chen (UC Irvine)

Robust perception is crucial for autonomous vehicle security. In this work, we design a practical adversarial patch attack against camera-based obstacle detection. We identify that the back of a box truck is an effective attack vector. We also improve attack robustness by considering a variety of input frames associated with the attack scenario. This demo includes videos that show our attack can cause end-to-end consequences on a representative autonomous driving system in a simulator.

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Demo #8: Identifying Drones Based on Visual Tokens

Ben Nassi (Ben-Gurion University of the Negev), Elad Feldman (Ben-Gurion University of the Negev), Aviel Levy (Ben-Gurion University of the Negev), Yaron Pirutin (Ben-Gurion University of the Negev), Asaf Shabtai (Ben-Gurion University of the Negev), Ryusuke Masuoka (Fujitsu System Integration Laboratories) and Yuval Elovici (Ben-Gurion University of the Negev)

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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Oblivious DNS over HTTPS (ODoH): A Practical Privacy Enhancement...

Sudheesh Singanamalla*†, Suphanat Chunhapanya*, Jonathan Hoyland*, Marek Vavruša*, Tanya Verma*, Peter Wu*, Marwan Fayed*, Kurtis Heimerl†, Nick Sullivan*, Christopher Wood* (*Cloudflare Inc. †University of Washington)

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Digital Technologies in Pandemic: The Good, the Bad and...

Moderator: Ahmad-Reza Sadeghi, TU Darmstadt, Germany Panelists: Mario Guglielmetti, Legal Officer, European Data Protection Supervisor* Jaap-Henk Hoepman, Radbaud University, The Netherlands Alexandra Dmitrienko, University of Würzburg, Germany, Farinaz Koushanfar, UCSD, USA *attending in his personal capacity

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