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.

翻译：红外检测因其卓越的抗干扰能力，已成为安全关键任务中的新兴技术。然而，近期研究表明，该技术易受物理可实现的对抗性补丁攻击，这为其实际应用带来了风险。为解决这一问题，我们首次研究了针对红外检测（尤其是人体检测）中对抗性补丁攻击的防御策略。我们提出了一种简洁的防御策略——基于补丁的遮挡感知检测（POD），该方法通过随机补丁高效地增强训练样本，并随后对其进行检测。POD不仅能稳健地检测人体，还能识别对抗性补丁的位置。令人惊讶的是，尽管计算效率极高，POD能够轻松泛化至训练过程中未见的最新对抗性补丁攻击。此外，由于数据增强效应，POD即使在干净（即无攻击）环境下也能提升检测精度。我们的评估表明，POD对各种形状和尺寸的对抗性补丁均具有鲁棒性。该基线方法的有效性被证明可作为实际红外人体检测系统的可行防御机制，为未来研究方向的探索铺平了道路。