Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

Susceptibility of neural networks to adversarial attack prompts serious safety concerns for lane detection efforts, a domain where such models have been widely applied. Recent work on adversarial road patches have successfully induced perception of lane lines with arbitrary form, presenting an avenue for rogue control of vehicle behavior. In this paper, we propose a modular lane verification system that can catch such threats before the autonomous driving system is misled while remaining agnostic to the particular lane detection model. Our experiments show that implementing the system with a simple convolutional neural network (CNN) can defend against a wide gamut of attacks on lane detection models. With a 10% impact to inference time, we can detect 96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98% of patch attacks while preserving accurate identification at least 95% of true lanes, indicating that our proposed verification system is effective at mitigating lane detection security risks with minimal overhead.

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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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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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On the Insecurity of SMS One-Time Password Messages against...

Zeyu Lei (Purdue University), Yuhong Nan (Purdue University), Yanick Fratantonio (Eurecom & Cisco Talos), Antonio Bianchi (Purdue University)

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