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Deep learning model security evaluation system and method

The application provides a deep learning model security evaluation system and method, comprising the following steps: S1, collecting information of a to-be-tested model and performing pretreatment; S2, testing the to-be-tested model to obtain a test result; the test comprises adversarial attack test, data poisoning robustness test and backdoor trigger reverse test; S3, summarizing all test results and performing analysis to output a final evaluation result. The application provides security analysis for a trainer of a deep learning model, realizes integration of various attack algorithms, including adversarial sample attack, backdoor attack and data poisoning attack, etc., analyzes performances before and after model attack and defense, enables a user to clearly understand performance changes of the user's model under various attack types, and enables the user to comprehensively evaluate the user's model on a unified platform and better grasp security of the model.
Owner:SHANGHAI JIAOTONG UNIV