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.