Takami Sato (UC Irvine) and Qi Alfred Chen (UC Irvine)

Deep Neural Network (DNN)-based lane detection is widely utilized in autonomous driving technologies. At the same time, recent studies demonstrate that adversarial attacks on lane detection can cause serious consequences on particular production-grade autonomous driving systems. However, the generality of the attacks, especially their effectiveness against other state-of-the-art lane detection approaches, has not been well studied. In this work, we report our progress on conducting the first large-scale empirical study to evaluate the robustness of 4 major types of lane detection methods under 3 types of physical-world adversarial attacks in end-to-end driving scenarios. We find that each lane detection method has different security characteristics, and in particular, some models are highly vulnerable to certain types of attack. Surprisingly, but probably not coincidentally, popular production lane centering systems properly select the lane detection approach which shows higher resistance to such attacks. In the near future, more and more automakers will include autonomous driving features in their products. We hope that our research will help as many automakers as possible to recognize the risks in choosing lane detection algorithms.

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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Preventing Kernel Hacks with HAKCs

Derrick McKee (Purdue University), Yianni Giannaris (MIT CSAIL), Carolina Ortega (MIT CSAIL), Howard Shrobe (MIT CSAIL), Mathias Payer (EPFL), Hamed Okhravi (MIT Lincoln Laboratory), Nathan Burow (MIT Lincoln Laboratory)

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F-PKI: Enabling Innovation and Trust Flexibility in the HTTPS...

Laurent Chuat (ETH Zurich), Cyrill Krähenbühl (ETH Zürich), Prateek Mittal (Princeton University), Adrian Perrig (ETH Zurich)

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