# Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction Source: [Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:27:18.233Z Authors: Abhijith Sharma, Phil Munz, Apurva Narayan Published: 2023-11-02T19:33:32.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()