Method, system, storage medium and equipment for deep neural network countermeasure defense based on feature denoising

Abstract

A feature denoising-based deep neural network confrontation defense method, system, storage medium, and device belong to the field of image-based deep learning defense against examples. In order to solve the problem that the current adversarial sample defense method using spatial domain filtering for feature denoising has incomplete denoising, which leads to the problem of poor adversarial sample defense effect. The invention designs a neural network model containing at least one feature denoising module. The feature denoising module includes 1x1 convolution, residual connection unit and denoising operation unit. The denoising operation first performs discrete wavelet transform on the feature map of the middle layer of the model , to separate useful information from noise information, and then perform denoising processing combining frequency domain filtering and spatial domain filtering on the high frequency components containing noise information, and finally reconstruct the feature map. Under the confrontation training, the method of the present invention can significantly improve the confrontation robustness in the face of adversarial sample attacks. Deep neural network adversarial defense mainly for graphs.

Description

Method, system and storage medium for deep neural network adversarial defense based on feature denoising and equipment technical field [0001] The present invention belongs to the field of image-based deep learning against sample defense, and in particular relates to a deep learning based feature denoising A neural network adversarial defense method, system, storage medium and device. Background technique In recent years, with the continuous development of artificial intelligence, deep learning automatically extracts data features, model tables by virtue of its automatic extraction. It has gradually become a research hotspot in the field of artificial intelligence, and is widely used in computer vision, speech recognition, Natural language processing and many other fields. However, artificial intelligence technologies such as deep learning are a double-edged sword. There are also inherent weaknesses. In deep learning techniques for images, attackers add micro- S...

AI Technical Summary

Problems solved by technology

[0004] The present invention solves the problem of incomplete denoising in the current adversarial sample defense method using spatial domain filtering for feature denoising, which in turn leads to poor adversarial sample defense effects

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