AdvETC: Adversarial Electrothermal Clothes for Attacking Infrared Object Detectors

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Abstract
Deep neural networks are vulnerable to adversarial attacks in infrared object detection. Previous infrared physical attacks have primarily targeted 2D pedestrian detection, showing limited practical effectiveness. Therefore, this paper proposes a design named Adversarial Electrothermal Clothes (AdvETC) based on a 3D human model, to attack infrared detectors from multiple viewpoints. This paper introduces a deep reinforcement learning-based optimization framework for AdvETC, enabling pedestrians to evade detection from various angles and distances. First, a 3D human model is created based on the infrared characteristics of humans in the physical world. Next, an artificial potential field is established within the search area, and a reward mechanism is introduced based on the detector's feedback and changes in the field's potential energy. Finally, trajectory points are smoothly connected using tangent-continuous Bézier curves to generate physically feasible and efficient adversarial trajectories. This paper is the first to apply graphene material in infrared adversarial attacks. By incorporating graphene-based heating fabric into the clothing based on the designed adversarial samples, the heating fabric heats up when powered, altering the infrared characteristics of the person’s surface. Due to the physical carrier's stealth, as well as the stability and continuous controllability of infrared radiation, our method significantly enhances attack effectiveness and reduces the complexity of practical implementation. Extensive experimental results show that, compared to other infrared physical attack methods, the AdvETC exhibit superior adversarial performance and robustness.

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