To solve the injection problem of poisoned samples in machine learning models,a poisoning defense algorithm infoGAN_Defense based on the original features of samples was proposed.Based on the production principle of poisoned samples,the extraction method of the original features of the poisoned samples was designed,and the training weight of the model on the ori-ginal features of the samples was improved.On this basis,poisoning defense was carried out using the invariance of the original features of the sample,the concepts of the original features of the samples and the artificial features were introduced,the cou-pling infoGAN structure was used to realize the separation and extraction of the sample features,and the machine learning model was retrained.By designing experiments on real datasets to evaluate the defense effect,the feasibility and effectiveness of the infoGAN_Defense algorithm are verified.
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