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Security Enhanced Sentence Similarity Computing Model Based on Convolutional Neural Network
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DOI：10.1109/ACCESS.2021.3099489.png)
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Abstract
En 中文
Deep learning model shows great advantages in various fields. However, researchers pay attention to how to improve the accuracy of the model, while ignoring the security considerations. The problem of controlling the judgment result of deep learning model by attack examples and then affecting the system decision-making is gradually exposed. In order to improve the security of sentence similarity analysis model, we propose a convolution neural network model based on attention mechanism. First of all, the mutual information between sentences is correlated by attention weighting. Then, it is input into improved convolutional neural network. In addition, we add attack examples to the input, which is generated by the firefly algorithm. In the attack example, we replace the words in the sentence to some extent, which results in the adversarial data with great semantic change but slight sentence structure change. To a certain extent, the addition of attack example increases the ability of model to identify adversarial data and improves the robustness of the model. Experimental results show that the accuracy, recall rate and F1 value of the model are due to other baseline models.
Keywords:
Feature extraction
Semantics
Security
Deep learning
Analytical models
Computational modeling
Convolution
Security enhancement mechanism
attack examples
convolutional neural network
attention mechanism
sentence similarity
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