我不认识你，但我能抓住你：针对目标检测器多样对抗补丁的实时防御
I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectors
AI总结：
提出NutNet模型，通过高效检测对抗补丁，实现对目标检测器隐藏攻击和出现攻击的实时双重防御，性能显著优于现有方法。
摘要：
深度神经网络（DNNs）凭借其无与伦比的性能彻底改变了计算机视觉领域，如目标检测。然而，现有研究表明DNNs容易受到对抗攻击。在物理世界中，攻击者可以利用对抗补丁实施隐藏攻击（HA），即给目标物体打补丁使其从检测器中消失；以及出现攻击（AA），即欺骗检测器将补丁误分类为特定物体。最近，针对检测器提出了许多防御方法以减轻对抗补丁的潜在威胁。然而，这些方法在泛化性、鲁棒性和效率方面仍存在局限性。大多数防御仅对HA有效，使检测器面对AA时仍然脆弱。在本文中，我们提出了NutNet，一种用于检测对抗补丁的创新模型，具有高泛化性、鲁棒性和效率。在数字和物理领域对包括YOLOv2-v4、SSD、Faster RCNN和DETR在内的六种检测器进行实验，结果表明我们提出的方法能有效防御HA和AA，且仅牺牲0.4%的干净性能。我们将NutNet与四种针对检测器的基线防御方法进行比较，对于HA和AA，我们方法的平均防御性能分别比现有方法高出2.4倍和4.7倍以上。此外，NutNet仅使推理时间增加8%，能满足检测系统的实时要求。NutNet的演示可在https://sites.google.com/view/nutnet获取。
英文摘要：
Deep neural networks (DNNs) have revolutionized the field of computer vision like object detection with their unparalleled performance. However, existing research has shown that DNNs are vulnerable to adversarial attacks. In the physical world, an adversary could exploit adversarial patches to implement a Hiding Attack (HA) which patches the target object to make it disappear from the detector, and an Appearing Attack (AA) which fools the detector into misclassifying the patch as a specific object. Recently, many defense methods for detectors have been proposed to mitigate the potential threats of adversarial patches. However, such methods still have limitations in generalization, robustness and efficiency. Most defenses are only effective against the HA, leaving the detector vulnerable to the AA. In this paper, we propose \textit{NutNet}, an innovative model for detecting adversarial patches, with high generalization, robustness and efficiency. With experiments for six detectors including YOLOv2-v4, SSD, Faster RCNN and DETR on both digital and physical domains, the results show that our proposed method can effectively defend against both the HA and AA, with only 0.4\% sacrifice of the clean performance. We compare NutNet with four baseline defense methods for detectors, and our method exhibits an average defense performance that is over 2.4 times and 4.7 times higher than existing approaches for HA and AA, respectively. In addition, NutNet only increases the inference time by 8\%, which can meet the real-time requirements of the detection systems. Demos of NutNet are available at: \url{https://sites.google.com/view/nutnet}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
