Recent studies reveal that deep neural network (DNN) based object detectors are vulnerable to adversarial attacks in the form of adding the perturbation to the images, leading to the wrong output of object detectors. Most current existing works focus on generating perturbed images, also called adversarial examples, to fool object detectors. Though the generated adversarial examples themselves can remain a certain naturalness, most of them can still be easily observed by human eyes, which limits their further application in the real world. To alleviate this problem, we propose a differential evolution based dual adversarial camouflage (DE_DAC) method, composed of two stages to fool human eyes and object detectors simultaneously. Specifically, we try to obtain the camouflage texture, which can be rendered over the surface of the object. In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene images, making human eyes difficult to distinguish. In the second stage, we design three loss functions to optimize the local texture, making object detectors ineffective. In addition, we introduce the differential evolution algorithm to search for the near-optimal areas of the object to attack, improving the adversarial performance under certain attack area limitations. Besides, we also study the performance of adaptive DE_DAC, which can be adapted to the environment. Experiments show that our proposed method could obtain a good trade-off between the fooling human eyes and object detectors under multiple specific scenes and objects.
翻译:最近的研究表明,基于深神经网络的物体探测器很容易受到对抗性攻击,其形式是将扰动器添加到图像中,导致物体探测器输出错误。目前大多数现有工作的重点是生成扰动图像,也称为对抗性实例,以欺骗物体探测器。虽然生成的对抗性实例本身可以保持一定的自然性,但大部分仍然很容易被人类眼睛观察到,这限制了它们进一步应用于现实世界。为了缓解这一问题,我们建议采用基于两种对抗性伪装(DE_DAC)的不同演化法,由两个阶段组成,即欺骗人类眼睛和物体探测器,同时产生错误的结果。具体地说,我们试图获得可在物体表面上提供的迷彩色纹。在第一阶段,我们优化全球的纹理,以尽量减少已形成物体与图像之间的差异,使人类的眼睛难以辨别。在第二阶段,我们设计三个损失函数来优化本地的纹理,使物体探测器变得无效。此外,我们引入了差异性演化算法,以寻找攻击目标的近优性区域,改进了在目标表面的迷惑性环境下,改进了反向性环境。此外,在某种实验性环境之下,还提出了一种改进了一种适应性能环境之下,在某种反动性研究之下,可以展示。