RPATTACK：对一般对象检测器的精细补丁攻击
RPATTACK: Refined Patch Attack on General Object Detectors
摘要：
如今，像YOLO和Faster R-CNN这样的通用对象检测器及其变体在许多应用中得到了广泛的利用。许多研究表明，这些检测器非常容易受到对抗性补丁攻击。以前的基于块的攻击对目标检测器产生的扰动区域非常大，这是攻击所不需要的，也是人眼可感知的。为了产生更少但更有效的扰动，我们提出了一种新的基于补丁的方法来攻击一般的对象检测器。首先，我们提出了一个补丁的选择和细化计划，找到最重要的攻击像素，并逐步消除无关紧要的扰动。然后，对于稳定的集成攻击，我们平衡检测器的梯度，以避免在训练阶段过度优化其中一个。我们的RPAttack可以为Yolo v4和Faster R-CNN实现100%的惊人漏检率，而在VOC 2007测试集上仅修改0.32%的像素。我们的代码可在https://github.com/VDIGPKU/RPAttack上获得。
英文摘要：
Nowadays, general object detectors like YOLO and Faster R-CNN as well as their variants are widely exploited in many applications. Many works have revealed that these detectors are extremely vulnerable to adversarial patch attacks. The perturbed regions generated by previous patch-based attack works on object detectors are very large which are not necessary for attacking and perceptible for human eyes. To generate much less but more efficient perturbation, we propose a novel patch-based method for attacking general object detectors. Firstly, we propose a patch selection and refining scheme to find the pixels which have the greatest importance for attack and remove the inconsequential perturbations gradually. Then, for a stable ensemble attack, we balance the gradients of detectors to avoid over-optimizing one of them during the training phase. Our RPAttack can achieve an amazing missed detection rate of 100% for both Yolo v4 and Faster R-CNN while only modifies 0.32% pixels on VOC 2007 test set. Our code is available at https://github.com/VDIGPKU/RPAttack.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
