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An Overview of Adversarial Attacks and Defenses

Kai Chen*, Jinwei Wang, Jiawei Zhang

Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Kai Chen. Email: email

Journal of Information Hiding and Privacy Protection 2022, 4(1), 15-24.


In recent years, machine learning has become more and more popular, especially the continuous development of deep learning technology, which has brought great revolutions to many fields. In tasks such as image classification, natural language processing, information hiding, multimedia synthesis, and so on, the performance of deep learning has far exceeded the traditional algorithms. However, researchers found that although deep learning can train an accurate model through a large amount of data to complete various tasks, the model is vulnerable to the example which is modified artificially. This technology is called adversarial attacks, while the examples are called adversarial examples. The existence of adversarial attacks poses a great threat to the security of the neural network. Based on the brief introduction of the concept and causes of adversarial example, this paper analyzes the main ideas of adversarial attacks, studies the representative classical adversarial attack methods and the detection and defense methods.


Cite This Article

APA Style
Chen, K., Wang, J., Zhang, J. (2022). An overview of adversarial attacks and defenses. Journal of Information Hiding and Privacy Protection, 4(1), 15-24.
Vancouver Style
Chen K, Wang J, Zhang J. An overview of adversarial attacks and defenses. J Inf Hiding Privacy Protection . 2022;4(1):15-24
IEEE Style
K. Chen, J. Wang, and J. Zhang "An Overview of Adversarial Attacks and Defenses," J. Inf. Hiding Privacy Protection , vol. 4, no. 1, pp. 15-24. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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