Xidian University

Chengjia Yan

Qingqi Pei

Yang Xiao

A method for defending against an adversarial sample in image classification includes: denoising, by an adversarial denoising network, an input image to acquire a reconstructed image; acquiring, by a target classification model, a predicted category probability distribution of the reconstructed image; acquiring, by the target classification model, a predicted category probability distribution of the original input image; calculating an adversarial score of the input image, and determining the input image as an adversarial sample or a benign sample according to a threshold; outputting a category prediction result of the reconstructed image if the input image is determined as the adversarial sample; and outputting a category prediction result of the original input image if the input image is determined as the benign sample. A system for defending against an adversarial sample in image classification, and a data processing terminal are further provided.

Method and system for defending against adversarial sample in image classification includes denoising by an adversarial denoising network, and data processing terminal

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Xi'an, China

Chengjia Yan