Adversarial patches threaten visual AI models in the real world. The number
of patches in a patch attack is variable and determines the attack's potency in
a specific environment. Most existing defenses assume a single patch in the
scene, and the multiple patch scenarios are shown to overcome them. This paper
presents a model-agnostic defense against patch attacks based on total
variation for image resurfacing (TVR). The TVR is an image-cleansing method
that processes images to remove probable adversarial regions. TVR can be
utilized solely or augmented with a defended model, providing multi-level
security for robust prediction. TVR nullifies the influence of patches in a
single image scan with no prior assumption on the number of patches in the
scene. We validate TVR on the ImageNet-Patch benchmark dataset and with
real-world physical objects, demonstrating its ability to mitigate patch
attack.

对抗性补丁威胁真实世界中的视觉 AI 模型。基于图像再表面化的总变差（TVR）方法提出了一种模型无关的对抗性补丁防御方法。TVR 能够单独使用或与防御模型相结合，为稳健预测提供多层次的安全保障，无论场景中补丁的数量如何。在 ImageNet-Patch 基准数据集和真实世界物体上验证了 TVR 的能力，证明了其减轻补丁攻击的能力。