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Deep learning model optimization method based on data defense

A deep learning and model technology, applied in neural learning methods, electrical digital data processing, biological neural network models, etc.

Pending Publication Date: 2020-11-03
深圳慕智科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

Today's anti-sample defense methods are still unable to achieve comprehensive defense, and can only be effective against specific attack methods

Method used

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  • Deep learning model optimization method based on data defense
  • Deep learning model optimization method based on data defense
  • Deep learning model optimization method based on data defense

Examples

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Embodiment Construction

[0024] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0025] This patent implements the optimization of the deep neural network model through adversarial training, and the specific key technologies involved include deep convolutional neural network (CNN), adversarial example (Adversarial Example), adversarial example defense technology, etc.

[0026] 1. Adversarial sample generation

[0027] In the present invention, we use the convolutional neural network as the main optimization target to generate adversarial examples for the data set in the form of pictures. Convolutional neural networks are a class of feed-forward neural networks that include convolutional computations and have deep structures. The convolutional neural network has the ability of representation learning, and can per...

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Abstract

The invention discloses a deep learning model optimization method based on data defense, which can find a relatively effective defense means when coping with different countermeasure sample attack methods. In order to optimize the model for the adversarial sample attack method, the data-level defense strategy mainly comprises the steps that adversarial samples are injected into a training data setin the training stage and then the model is retrained, or the samples are modified in the prediction stage, reconstruction is conducted, and the converted adversarial samples are input into an original model to be predicted. An open source adversarial sample generation tool is used to generate adversarial samples for the to-be-tested model and the target data set, and success rates of the model on the specified data set are compared before and after the adversarial samples are generated.

Description

technical field [0001] The invention belongs to the field of intelligent software testing, and in particular relates to the optimization of deep learning models. For the model that needs to be optimized, the data-level optimization method is used to modify the training and test data to realize the optimization of the model, so that the model can obtain higher accuracy. Background technique [0002] In recent years, deep learning theory and technology have continued to mature, and good application results have been achieved in artificial intelligence, big data analysis, and security detection. played a key role. However, while bringing convenience, deep learning itself also has certain security problems. Such as the adversary's attack and the data privacy problem, which caused great concern in the field of security. The image field is a field where deep learning testing develops rapidly. This article discusses the optimization of deep learning models from the perspective o...

Claims

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Application Information

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IPC IPC(8): G06F11/36G06N3/04G06N3/08
CPCG06F11/3672G06N3/08G06N3/044G06N3/045
Inventor 陈振宇顾逸飞吕军刘佳玮
Owner 深圳慕智科技有限公司
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