论文标题
DPATTACK：针对通用对象检测的扩散补丁攻击
DPAttack: Diffused Patch Attacks against Universal Object Detection
论文作者
论文摘要
最近，深层神经网络（DNN）已广泛地用于对象检测中，例如更快的RCNN，YOLO，Centernet。但是，最近的研究表明，DNN容易受到对抗攻击的影响。针对对象检测的对抗攻击可以分为两类，全像素攻击和补丁攻击。尽管这些攻击在图像中的大量像素中增加了扰动，但我们提出了一种扩散的补丁攻击（\ textbf {dpattack}），以通过小星形或网格形状的漫射斑块成功地欺骗对象检测器，这只会更改少量像素。实验表明，我们的DPATTACK可以成功地用扩散的补丁欺骗大多数对象探测器，并且我们在阿里巴巴天奇竞赛中获得了第二名：阿里巴巴 - tsinghua对象检测中的对抗性挑战。我们的代码可以从https://github.com/wu-shudeng/dpattack获得。
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.
