近年来，基于深度神经网络的应用日益广泛，然而深度神经网络容易受到由输入数据设计的微小扰动而带来的对抗性攻击，导致网络的错误输出，给智能系统的部署带来安全隐患.为了提高智能系统的抗风险能力，有必要对存在风险的扰动生成方法展开研究.快速特征欺骗（fast feature fool，FFF）是面向视觉任务的一种有效的通用扰动生成方法.考虑了输入图像在网络中的实际激活状态，以最大化原始图像和对抗样本之间的特征差异作为生成扰动的目标函数；同时考虑不同卷积层对于生成扰动的不同影响，在生成扰动的目标函数中，对不同卷积层对应的项加以不同权重.实验结果表明，改进的FFF方法攻击成功率更高，同时也具备更强的跨模型攻击能力.
Although deep neural networks have been widely applied in recent years, they are readily fooled by adversarial input perturbations which are imperceptible to humans. Such vulnerability to adversarial attacks has imposed threats for system deployment in security-crucial setting, thus it is necessary to study the risky generation method of perturbations to boost the anti-risk capability. As a universal perturbation, fast feature fool (FFF) is an effective attacking method for visual tasks. Beyond solely mixing the convolutional layer's output irrespective of the input activation status, this paper improves the FFF method by maximizing the feature difference between the input image and corresponding adversarial image during which the contributions of multiple convolutional layers are weighted differently. Experimental results demonstrate that the improved FFF actually has obtained higher success attacking rate and stronger cross-model transfer ability than the original one.
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