Invisible Reflections: Leveraging Infrared Laser Reflections to Target
  Traffic Sign Perception
Takami Sato Sri Hrushikesh Varma Bhupathiraju Michael Clifford Takeshi Sugawara Qi Alfred Chen Sara Rampazzi
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 projected colored patches to signs, that cause CAV
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 an effective physical-world attack that
leverages the sensitivity of filterless image sensors and the properties of
Infrared Laser Reflections (ILRs), which are invisible to humans. The attack is
designed to affect CAV cameras and perception, undermining traffic sign
recognition by inducing misclassification. In this work, we formulate the
threat model and requirements for an ILR-based traffic sign perception attack
to succeed. We evaluate the effectiveness of the ILR attack with real-world
experiments against two major traffic sign recognition architectures on four
IR-sensitive cameras. Our black-box optimization methodology allows the attack
to achieve up to a 100% attack success rate in indoor, static scenarios and a
>80.5% attack success rate in our outdoor, moving vehicle scenarios. We find
the latest state-of-the-art certifiable defense is ineffective against ILR
attacks as it mis-certifies >33.5% of cases. To address this, we propose a
detection strategy based on the physical properties of IR laser reflections
which can detect 96% of ILR attacks.