Adversarial infrared blocks: A multi-view black-box attack to thermal infrared detectors in physical world

Abstract

Thermal infrared detectors have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. Recent works use bulb plate, "QR" suit, and infrared patches as physical perturbations to perform white-box attacks on thermal infrared detectors, which are effective but not practical for real-world scenarios. Some researchers have tried to utilize hot and cold blocks as physical perturbations for black-box attacks on thermal infrared detectors. However, this attempts has not yielded robust and multi-view physical attacks, indicating limitations in the approach. To overcome the limitations of existing approaches, we introduce a novel black-box physical attack method, called adversarial infrared blocks (AdvIB). By optimizing the physical parameters of the infrared blocks and deploying them to pedestrians from multiple views, including the front, side, and back, AdvIB can execute robust and multi-view attacks on thermal infrared detectors. Our physical tests show that the proposed method achieves a success rate of over 80% under most distance and view conditions, validating its effectiveness. For stealthiness, our method involves attaching the adversarial infrared block to the inside of clothing, enhancing its stealthiness. Additionally, we perform comprehensive experiments and compare the experimental results with baseline to verify the robustness of our method. In summary, AdvIB allows for potent multi-view black-box attacks, profoundly influencing ethical considerations in today's society. Potential consequences, including disasters from technology misuse and attackers' legal liability, highlight crucial ethical and security issues associated with AdvIB. Considering these concerns, we urge heightened attention to the proposed AdvIB. Our code can be accessed from the following link: https://github.com/ChengYinHu/AdvIB.git.

Keywords: Adversarial infrared blocks; Effectiveness; Multi-view black-box attacks; Robustness; Stealthiness; Thermal infrared detectors.