Artificial intelligence optimization system and method adopting adversarial resistance training

Abstract

The invention discloses an artificial intelligence optimization system and method adopting adversarial training, and belongs to the field of machine learning. The method comprises the following steps:carrying out feature extraction on acquired data in an original data set, and training a neural network model which is not subjected to adversarial training to obtain an identification model; Takingthe identification model as a parameter for calculating the fitness, and generating confrontation samples by a genetic algorithm based on the calculated parameter and the extracted characteristics toobtain all the generated confrontation samples; Mixing all generated adversarial samples with the samples subjected to feature extraction, and performing supervised training of the identification model again as a new data set to obtain an identification model subjected to supervised training; Testing the supervised and trained recognition model, if the supervised and trained recognition model meets a given requirement, obtaining a finally trained recognition model, and if not, adjusting parameters of a genetic algorithm, and then generating a confrontation sample again for training. The methodis used for training the neural network model against resistance, and the model security is improved.

Description

technical field [0001] An artificial intelligence optimization system and method using confrontational training, used for confrontational training of neural network models, belongs to the field of machine learning. Background technique [0002] In recent years, machine learning has been widely used and has achieved good application results in many fields, such as malicious email detection, malicious program detection, image recognition, face recognition, image classification, unmanned driving, etc., which are closely related to people's daily life There are examples of machine learning in every field. Therefore, machine learning gradually penetrates into people's daily life and becomes a key technology to improve people's living standards. However, while machine learning has brought great help to people's learning and life, there are still many security problems in machine learning algorithms. In early spam detection systems and intrusion detection systems, attackers target...

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Problems solved by technology

[0007] In view of the problems of the above research, the purpose of the present invention is to provide an artificial intelligence optimization system and method that adopts confrontational training. In the existing confrontational training, the computational complexity of resistance training is large, and the shortcomings of passive defense result in high cost and low efficiency. Problems such as poor stickiness

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