Yuzhe Ma, Jon Sharp, Ruizhe Wang, Earlence Fernandes, and Jerry Zhu (University of Wisconsin–Madison)

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this work, we demonstrate planning-based attacks on Forward Collision Warning — a machine-human hybrid system that uses KF. Based on our work published at the AAAI2021 conference, we use an MPC-based algorithm and show how an attacker can sequentially perturb vision measurements to change the FCW alert signals at desired points in time. We simulate our attack on CARLA using standard test protocols from the National Highway Traffic Safety Administration.

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Rosita: Towards Automatic Elimination of Power-Analysis Leakage in Ciphers

Madura A. Shelton (University of Adelaide), Niels Samwel (Radboud University), Lejla Batina (Radboud University), Francesco Regazzoni (University of Amsterdam and ALaRI – USI), Markus Wagner (University of Adelaide), Yuval Yarom (University of Adelaide and Data61)

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What Remains Uncaught?: Characterizing Sparsely Detected Malicious URLs on...

Sayak Saha Roy, Unique Karanjit, Shirin Nilizadeh (The University of Texas at Arlington)

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Improving Signal's Sealed Sender

Ian Martiny (University of Colorado Boulder), Gabriel Kaptchuk (Boston University), Adam Aviv (The George Washington University), Dan Roche (U.S. Naval Avademy), Eric Wustrow (University of Colorado Boulder)

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Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

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