论文标题
用一个补丁抑制：重新访问通用对抗补丁攻击对象检测
Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection
论文作者
论文摘要
基于对抗斑块的攻击旨在欺骗一个有意产生的噪声的神经网络，该网络集中在输入图像的特定区域中。在这项工作中，我们对不同的贴片生成参数进行了深入的分析，包括初始化，贴剂大小，尤其是在训练过程中将贴剂放置在图像中。我们专注于对象消失的攻击，并用Yolov3作为白色框设置中攻击的模型运行实验，并使用可可数据集中的图像。我们的实验表明，在训练期间，将斑块插入大小增加的窗口内，与固定位置相比，攻击强度显着提高。当斑块在训练过程中随机放置时，获得了最佳结果，而贴片位置则在批处理中也有所不同。
Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concentrated in a particular region of an input image. In this work, we perform an in-depth analysis of different patch generation parameters, including initialization, patch size, and especially positioning a patch in an image during training. We focus on the object vanishing attack and run experiments with YOLOv3 as a model under attack in a white-box setting and use images from the COCO dataset. Our experiments have shown, that inserting a patch inside a window of increasing size during training leads to a significant increase in attack strength compared to a fixed position. The best results were obtained when a patch was positioned randomly during training, while patch position additionally varied within a batch.
