Deep learning model optimization method based on network addition/modification

An optimization method and network technology, applied in the field of optimization of deep learning models

Pending Publication Date: 2020-11-03
深圳慕智科技有限公司
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Problems solved by technology

Today's anti-sample defense methods are still unable to achieve comprehe

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  • Deep learning model optimization method based on network addition/modification
  • Deep learning model optimization method based on network addition/modification
  • Deep learning model optimization method based on network addition/modification

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Embodiment Construction

[0022] 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.

[0023] 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.

[0024] 1. Adversarial sample generation

[0025] 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 relates to a deep learning model optimization method based on network addition/modification, in particular to an optimization method integrating a model level in the field of deep learning model optimization, and specifies an optimization strategy for adversarial samples by adopting an evaluation feedback mechanism. A defense strategy is formulated by evaluating a model optimized byusing a defense method and evaluating a feedback mechanism, and an optimal defense means is selected for coping with different attack methods. In order to optimize the model for the adversarial sampleattack method, the defense strategy of the model level is to modify the network, and the structure of the original DNN model is modified in the training stage, or the original model is not changed, and an external model is used as an additional network, so the defended DNN classifier can detect the adversarial sample or identify the adversarial sample as a correct label.

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 model-level optimization method is used to modify the model or add the network 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 issues. 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 ...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06K9/62G06F11/36
CPCG06N3/08G06F11/3672G06N3/044G06N3/045G06F18/241
Inventor 房春荣顾逸飞吕军刘佳玮
Owner 深圳慕智科技有限公司
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