| 基于生成对抗网络的抗泄露加密算法研究 |
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| 引用本文： | 李西明,吴嘉润,吴少乾,郭玉彬,马莎. 基于生成对抗网络的抗泄露加密算法研究[J]. 计算机工程与应用, 2020, 56(10): 69-74. DOI: 10.3778/j.issn.1002-8331.1902-0062 |
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| 作者姓名： | 李西明 吴嘉润 吴少乾 郭玉彬 马莎 |
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| 作者单位： | 华南农业大学 数学与信息学院，广州 510642 |
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| 基金项目： | 广东省特支计划;广东省自然科学基金杰出青年基金;国家自然科学基金;广州市珠江科技新星项目 |
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| 摘 要： | 生成对抗网络（Generative Adversarial Networks，GANs）是一种深度学习模型，通过与辨别模型的对抗获得逐渐完善的生成模型，用以产生真假难辨的数据，而利用生成对抗网络实现加密算法是一个新的研究方向。在16位密钥对称加密方案下，对Abadi等人的基本加密通信模型做了抗泄漏加密通信测试，发现了利用生成对抗网络实现抗泄露加密通信的可能性。对通信双方和敌手的神经网络模型进行了改进，通过修改系统的激活函数，获得3比特密钥泄露情况下的加密算法模型，通过增加解密方和敌手模型的复杂度可提高通信的稳定性。在模型中增加批规格化处理，进一步提升了抗泄露加密通信能力。最终可以在8位泄漏的情况下，保证通信双方正常通信且敌手无法获取秘密信息。为抗泄露加密通信问题提供了一种全新的解决方案，并通过实验证明了方案的可行性。
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| 关 键 词： | 抗泄露 生成对抗网络 批规格化 全连接神经网络 卷积神经网络 |
Key Resilient Encryption Algorithm Based on Generative Adversarial Networks |
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| LI Ximing,WU Jiarun,WU Shaoqian,GUO Yubin,MA Sha. Key Resilient Encryption Algorithm Based on Generative Adversarial Networks[J]. Computer Engineering and Applications, 2020, 56(10): 69-74. DOI: 10.3778/j.issn.1002-8331.1902-0062 |
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| Authors: | LI Ximing WU Jiarun WU Shaoqian GUO Yubin MA Sha |
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| Affiliation: | College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China |
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| Abstract: | Generative Adversarial Networks（GANs） is a kind of deep learning model. Through adversarial training with discriminative model, a gradually improved generative model can be obtained to generate data which are difficult to distinguish between true and false. And using GANs to realize encryption algorithm is a new research direction. Firstly, under the 16 bit key symmetric encryption scheme, this paper tests the key resilient encryption communication of the basic encryption communication model built by Abadi et al., and finds the possibility of realizing the key resilient encryption communication by using GANs. Then, the neural network model of both parties and the adversary is improved by modifying the activation function of the network, and obtains the encryption algorithm model in the case of 3 bit key leakage. The communication stability can be improved by increasing the complexity of the decrypter and the adversary model. Then, adding batch normalization in the model can further improve the ability of key resilient encryption communication. Finally, in the case of 8 bit leakage, it can ensure the normal communication between the two sides of the communication and the adversary cannot obtain the secret information. This paper provides a new solution to the problem of key resilient encryption communication and proves the feasibility of the solution through experiments. |
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| Keywords: | key resilient generative adversarial networks batch normalization fully connected neural networks convolutional neural networks |
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