  Infrared detection is an emerging technique for safety-critical tasks owing
to its remarkable anti-interference capability. However, recent studies have
revealed that it is vulnerable to physically-realizable adversarial patches,
posing risks in its real-world applications. To address this problem, we are
the first to investigate defense strategies against adversarial patch attacks
on infrared detection, especially human detection. We propose a straightforward
defense strategy, patch-based occlusion-aware detection (POD), which
efficiently augments training samples with random patches and subsequently
detects them. POD not only robustly detects people but also identifies
adversarial patch locations. Surprisingly, while being extremely
computationally efficient, POD easily generalizes to state-of-the-art
adversarial patch attacks that are unseen during training. Furthermore, POD
improves detection precision even in a clean (i.e., no-attack) situation due to
the data augmentation effect. Our evaluation demonstrates that POD is robust to
adversarial patches of various shapes and sizes. The effectiveness of our
baseline approach is shown to be a viable defense mechanism for real-world
infrared human detection systems, paving the way for exploring future research
directions.
