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

Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction