Open Access

ARTICLE

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:

Journal of Information Hiding and Privacy Protection 2022, 4(1), 15-24. https://doi.org/10.32604/jihpp.2022.029006

Received 23 February 2022; Accepted 11 May 2022; Issue published 17 June 2022

Abstract

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.

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Keywords

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