# DPAttack: Diffused Patch Attacks against Universal Object Detection

Source: [DPAttack: Diffused Patch Attacks against Universal Object Detection](<https://www.sota2.com/research/paper/68fa689c1e209131cea1d0d8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:40:59.400Z

Authors: Shudeng Wu, Tao Dai, Shu-Tao Xia

Published: 2020-10-16T04:48:24.000Z

## Abstract

Recently, deep neural networks (DNNs) have been widely and successfully used in Object Detection, e.g. Faster RCNN, YOLO, CenterNet. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Adversarial attacks against object detection can be divided into two categories, whole-pixel attacks and patch attacks. While these attacks add perturbations to a large number of pixels in images, we proposed a diffused patch attack (\\textbf{DPAttack}) to successfully fool object detectors by diffused patches of asteroid-shaped or grid-shape, which only change a small number of pixels. Experiments show that our DPAttack can successfully fool most object detectors with diffused patches and we get the second place in the Alibaba Tianchi competition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. Our code can be obtained from https://github.com/Wu-Shudeng/DPAttack.

## 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](<https://arxiv.org/abs/2010.11679>)

[PDF](<https://arxiv.org/pdf/2010.11679.pdf>)