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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To Err.Is Human: Characterizing the Threat of Unintended URLs...

Beliz Kaleli (Boston University), Brian Kondracki (Stony Brook University), Manuel Egele (Boston University), Nick Nikiforakis (Stony Brook University), Gianluca Stringhini (Boston University)

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Demo #9: Dynamic Time Warping as a Tool for...

Mars Rayno (Colorado State University) and Jeremy Daily (Colorado State University)

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Flexsealing BGP Against Route Leaks: Peerlock Active Measurement and...

Tyler McDaniel (University of Tennessee, Knoxville), Jared M. Smith (University of Tennessee, Knoxville), Max Schuchard (University of Tennessee, Knoxville)

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