Generative adversarial network-based adversarial attack sample generation method

Abstract

The invention provides a generative adversarial network-based adversarial attack sample generation method. The method comprises the steps of S1, collecting a training sample image; s2, preprocessing and classification definition; s3, constructing a training framework, wherein the training framework comprises a generator, a discriminator and a target attack network; s4, generating random noise anda random condition vector based on the training sample images after preprocessing and classification definition; s5, taking the random noise and the random condition vector as input of a generator inthe training framework, training the generator and a discriminator of the training framework, and storing trained training framework parameters; and S6, finishing training, and generating an adversarial attack sample. The method provided by the invention has relatively strong data learning capability and relatively high robustness, overcomes the defect that an existing method lacks learning capability for data distribution based on an optimization equation and matrix measurement of a simple pixel space, and improves the generation quality and efficiency of an anti-attack sample.

Description

technical field [0001] The present invention relates to the technical field of generating adversarial attack samples, and more specifically, to a method for generating adversarial attack samples based on generative adversarial networks. Background technique [0002] Deep learning occupies a core position in today's rapidly developing field of machine learning and artificial intelligence. However, modern visual deep neural networks (DNN) cannot effectively resist attacks from adversarial attack samples. The perturbed input samples are very vulnerable, and this kind of adversarial attack samples can mislead the deep neural network to produce wrong output results. [0003] At present, the academic community has proposed a variety of attack strategies for generating adversarial attack samples. In the research of generating adversarial attack samples, a series of available and effective attack models have been proposed. These models can be classified into the following categories...

AI Technical Summary

Problems solved by technology

[0010] In order to overcome the defects that most of the existing adversarial attack sample generation methods are based on optimization equations and simple pixel space matrix metrics, lack of learning ability for data distribution, and have low robustness, the present invention provides a method based on generative adversarial network The generation method of adversarial attack samples improves the robustness of classifiers and the quality and efficiency of adversarial attack samples generation, and promotes the development of adversarial attack research field

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