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A Survey on Adversarial Example

Jiawei Zhang*, Jinwei Wang

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

* Corresponding Author: Jiawei Zhang. Email: email

Journal of Information Hiding and Privacy Protection 2020, 2(1), 47-57. https://doi.org/10.32604/jihpp.2020.010462

Abstract

In recent years, deep learning has become a hotspot and core method in the field of machine learning. In the field of machine vision, deep learning has excellent performance in feature extraction and feature representation, making it widely used in directions such as self-driving cars and face recognition. Although deep learning can solve large-scale complex problems very well, the latest research shows that the deep learning network model is very vulnerable to the adversarial attack. Add a weak perturbation to the original input will lead to the wrong output of the neural network, but for the human eye, the difference between origin images and disturbed images is hardly to be notice. In this paper, we summarize the research of adversarial examples in the field of image processing. Firstly, we introduce the background and representative models of deep learning, then introduce the main methods of the generation of adversarial examples and how to defend against adversarial attack, finally, we put forward some thoughts and future prospects for adversarial examples.

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Cite This Article

APA Style
Zhang, J., Wang, J. (2020). A survey on adversarial example. Journal of Information Hiding and Privacy Protection, 2(1), 47-57. https://doi.org/10.32604/jihpp.2020.010462
Vancouver Style
Zhang J, Wang J. A survey on adversarial example. J Inf Hiding Privacy Protection . 2020;2(1):47-57 https://doi.org/10.32604/jihpp.2020.010462
IEEE Style
J. Zhang and J. Wang, "A Survey on Adversarial Example," J. Inf. Hiding Privacy Protection , vol. 2, no. 1, pp. 47-57. 2020. https://doi.org/10.32604/jihpp.2020.010462



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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