论文标题
对象注意的不靶向对抗性攻击
Object-Attentional Untargeted Adversarial Attack
论文作者
论文摘要
深度神经网络正面临对抗攻击的严重威胁。大多数现有的黑框攻击通过生成全局扰动或本地补丁来欺骗目标模型。但是，在对抗性示例中，全局扰动和本地贴片都很容易引起令人讨厌的视觉伪像。与图像的一些平滑区域相比，对象区域通常具有更多的边缘和更复杂的纹理。因此，对它的小扰动将更加不可感知。另一方面，对象区域无疑是对分类任务的图像的决定性部分。在这两个事实的驱动下，我们提出了一种针对性的对抗性攻击方法，以实现非目标攻击。具体而言，我们首先通过从Yolov4与HVPNET的显着对象检测（SOD）区域相交的对象检测区域来生成对象区域。此外，我们设计了一种激活策略，以避免由不完整的草皮引起的反应。然后，我们仅通过利用简单的黑盒对抗攻击（SIMBA）对检测到的对象区域进行对抗攻击。为了验证所提出的方法，我们通过在本文中提取包含来自Imagenet-1k的可可定义的对象的所有图像来创建一个唯一的数据集，该对象在本文中命名为可可还原 - imagenet。 Imagenet-1K和可可还原imagenet的实验结果表明，在各种系统设置下，我们的方法产生了对抗性示例，同时，与包括SIMBA在内的最先进的方法相比，与最先进的方法相比，可节省高达24.16 \％的查询预算。
Deep neural networks are facing severe threats from adversarial attacks. Most existing black-box attacks fool target model by generating either global perturbations or local patches. However, both global perturbations and local patches easily cause annoying visual artifacts in adversarial example. Compared with some smooth regions of an image, the object region generally has more edges and a more complex texture. Thus small perturbations on it will be more imperceptible. On the other hand, the object region is undoubtfully the decisive part of an image to classification tasks. Motivated by these two facts, we propose an object-attentional adversarial attack method for untargeted attack. Specifically, we first generate an object region by intersecting the object detection region from YOLOv4 with the salient object detection (SOD) region from HVPNet. Furthermore, we design an activation strategy to avoid the reaction caused by the incomplete SOD. Then, we perform an adversarial attack only on the detected object region by leveraging Simple Black-box Adversarial Attack (SimBA). To verify the proposed method, we create a unique dataset by extracting all the images containing the object defined by COCO from ImageNet-1K, named COCO-Reduced-ImageNet in this paper. Experimental results on ImageNet-1K and COCO-Reduced-ImageNet show that under various system settings, our method yields the adversarial example with better perceptual quality meanwhile saving the query budget up to 24.16\% compared to the state-of-the-art approaches including SimBA.
