【摘要】 对抗样本图像能欺骗深度学习网络,亟待对抗样本防御机制以增强深度学习模型的安全性。C&W攻击是目前较热门的一种白盒攻击算法,它产生的对抗样本具有图像质量高、可转移、攻击性强、难防御等特点。本文以C&W攻击生成的对抗样本为研究对象,采用数字图像取证的思路,力图实现C&W对抗样本的检测,拒绝对抗样本输入深度学习网络。基于对抗样本中的对抗扰动易被破坏的假设,我们设计了基于FFDNet滤波器的检测算法。具体来说, FFDNet是一种基于深度卷积网络CNN的平滑滤波器,它能破坏对抗扰动,导致深度学习模型对对抗样本滤波前后的输出不一致。我们判断输出不一致的待测图像为C&W对抗样本。我们在ImageNet-1000图像库上针对经典的ResNet深度网络生成了6种C&W对抗样本。实验结果表明本文方法能较好地检测C&W对抗样本。相较于已有工作,本文方法不仅极大地降低了虚警率,而且提升了C&W对抗样本的检测准确率。更多还原

【Abstract】 Adversarial images which can fool Deep neural networks have attracted researchers to focus on how to harden DNNs against adversarial attacks. Among typical attack algorithms, the C&W attack is one of the strongest attacks, which ensures the attack success rates yet causes less adversarial perturbations on the original image, and is taken as a benchmark in defense attempts. In this paper, we employ the blind forensic methodology to detect C&W adversarial images, which aims to avoid adversarial inputs for deep neural networks. Supposing that the adversarial perturbations are easily damaged by some image processing operations, we proposed a detecting method by using the fast and flexible de-noising convolution neural network called FFDNet. Specially, we compare the model’s prediction on the test image and its filtered version. If the original and filtered inputs produce substantially different outputs from the model, the test image is likely to be adversarial. We employ ResNet as the targeted network, and generate 6 kinds of C&W adversarial images on ImageNet-1000 database. Experimental results show that the proposed method is effective in the detection of C&W adversarial images, and outperforms state-of-the-arts in terms of false positive rates and true positive rates.更多还原