论文标题
使用频率注意使对抗斑块强大针对人探测器
Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector
论文作者
论文摘要
深度神经网络（DNN）容易受到对抗攻击的影响。特别是，可以通过将特定的对抗贴剂应用于图像来攻击对象探测器。但是，由于补丁在预处理过程中缩小，因此使用对抗斑块来攻击对象探测器的大多数现有方法都会降低中小型目标的攻击成功率。本文提出了一个频率模块（FRAN），这是用于指导贴片生成的频域注意模块。这是第一个引入频域注意的研究，以优化对抗斑块的攻击能力。我们的方法将中小型目标的攻击成功率分别提高了4.18％和3.89％，而最先进的攻击方法是为了欺骗人类探测器而攻击Yolov3而不降低大目标的攻击成功率。
Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image. However, because the patch shrinks during preprocessing, most existing approaches that employ adversarial patches to attack object detectors would diminish the attack success rate on small and medium targets. This paper proposes a Frequency Module(FRAN), a frequency-domain attention module for guiding patch generation. This is the first study to introduce frequency domain attention to optimize the attack capabilities of adversarial patches. Our method increases the attack success rates of small and medium targets by 4.18% and 3.89%, respectively, over the state-of-the-art attack method for fooling the human detector while assaulting YOLOv3 without reducing the attack success rate of big targets.
