Takami Sato (University of California, Irvine), Sri Hrushikesh Varma Bhupathiraju (University of Florida), Michael Clifford (Toyota InfoTech Labs), Takeshi Sugawara (The University of Electro-Communications), Qi Alfred Chen (University of California, Irvine), Sara Rampazzi (University of Florida)
DENSO Best Demo Award Winner ($100 cash prize)!
All vehicles must follow the rules that govern traffic behavior, regardless of whether the vehicles are human-driven or Connected, Autonomous Vehicles (CAVs). Road signs indicate locally active rules, such as speed limits and requirements to yield or stop. Recent research has demonstrated attacks, such as adding stickers or dark patches to signs, that cause CAV sign misinterpretation, resulting in potential safety issues. Humans can see and potentially defend against these attacks. But humans can not detect what they can not observe. We have developed the first physical-world attack against CAV traffic sign recognition systems that is invisible to humans. Utilizing Infrared Laser Reflection (ILR), we implement an attack that affects CAV cameras, but humans can not perceive. In this work, we formulate the threat model and requirements for an ILR-based sign perception attack. Next, we evaluate attack effectiveness against popular, CNN-based traffic sign recognition systems. We demonstrate a 100% success rate against stop and speed limit signs in our laboratory evaluation. Finally, we discuss the next steps in our research.