A deep learning classification method with the function of defending against sample attack
A technology against samples and classification methods, applied in neural architecture, biological neural network models, etc., can solve problems such as single attack, lack of universality, etc., to achieve the effect of improving performance robustness
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[0058] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.
[0059] The device for realizing the classification method of the present invention is a three-party game model based on a generative confrontation network, and its structure is as follows: figure 1 As shown, it mainly includes three modules: 1) The function of the attack generation model (Attack Generator, AG) is to automatically generate an adversarial sample x with as little disturbance as possible and as strong attack capability as possible adv , whose input consists of normal samples x nor , the real class label y of the sample, and the noise z; 2) The function of t...
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