关键词: 对抗样本, 卷积神经网络, 语义信息, 黑盒攻击

Abstract:

Convolutional neural network is a deep neural network with powerful feature extraction capabilities, it has been widely used in many fields. However, recent research shows that convolutional neural networks are vulnerable to adversarial attacks. Different from the traditional method of iteratively generating anti-perturbation by gradient, this paper proposes a color model-based method for generating semantic adversarial samples, which uses the shape preference characteristics of human vision and convolution model in object recognition, and generates the anti-disturbance sample by disturbing the color channel based on the transformation of color model. In the process of sample generation, it does not need the network parameters, loss function or related structure information of the target model, but only relies on the transformation of the color model and random disturbance of channel information. Therefore, it is a counter sample that can complete the black box attack.

Key words: adversarial examples, convolutional neural network, semantic feature, black-box attack