• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Generative Adversarial Networks: A Literature Review


The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of “generative” and “adversarial”, researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.


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Cite this article

[IEEE Style]
J. Cheng, Y. Yang, X. Tang, N. Xiong, Y. Zhang, F. Lei, "Generative Adversarial Networks: A Literature Review," KSII Transactions on Internet and Information Systems, vol. 14, no. 12, pp. 4625-4647, 2020. DOI: 10.3837/tiis.2020.12.001.

[ACM Style]
Jieren Cheng, Yue Yang, Xiangyan Tang, Naixue Xiong, Yuan Zhang, and Feifei Lei. 2020. Generative Adversarial Networks: A Literature Review. KSII Transactions on Internet and Information Systems, 14, 12, (2020), 4625-4647. DOI: 10.3837/tiis.2020.12.001.

[BibTeX Style]
@article{tiis:24135, title="Generative Adversarial Networks: A Literature Review", author="Jieren Cheng and Yue Yang and Xiangyan Tang and Naixue Xiong and Yuan Zhang and Feifei Lei and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.12.001}, volume={14}, number={12}, year="2020", month={December}, pages={4625-4647}}