Next Article in Journal
Nonuniform Bessel-Based Radiation Distributions on A Spherically Curved Boundary for Modeling the Acoustic Field of Focused Ultrasound Transducers
Next Article in Special Issue
An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks
Previous Article in Journal
Electrospun Nanometer to Micrometer Scale Biomimetic Synthetic Membrane Scaffolds in Drug Delivery and Tissue Engineering: A Review
Previous Article in Special Issue
An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet
Review

Review of Artificial Intelligence Adversarial Attack and Defense Technologies

by , *,†, and
The School of Information and Software Enginerring, University of Electronic Science and Technology of China, Chengdu 610054, China
*
Author to whom correspondence should be addressed.
Current address: No. 4, Section 2, Jianshe North Road, Chenghua District, Chengdu 610054, China.
Appl. Sci. 2019, 9(5), 909; https://doi.org/10.3390/app9050909
Received: 19 January 2019 / Revised: 20 February 2019 / Accepted: 22 February 2019 / Published: 4 March 2019
(This article belongs to the Special Issue Advances in Deep Learning)
In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools. View Full-Text
Keywords: artificial intelligence; deep learning; adversarial sample; adversarial attack; defense method artificial intelligence; deep learning; adversarial sample; adversarial attack; defense method
Show Figures

Figure 1

MDPI and ACS Style

Qiu, S.; Liu, Q.; Zhou, S.; Wu, C. Review of Artificial Intelligence Adversarial Attack and Defense Technologies. Appl. Sci. 2019, 9, 909. https://doi.org/10.3390/app9050909

AMA Style

Qiu S, Liu Q, Zhou S, Wu C. Review of Artificial Intelligence Adversarial Attack and Defense Technologies. Applied Sciences. 2019; 9(5):909. https://doi.org/10.3390/app9050909

Chicago/Turabian Style

Qiu, Shilin, Qihe Liu, Shijie Zhou, and Chunjiang Wu. 2019. "Review of Artificial Intelligence Adversarial Attack and Defense Technologies" Applied Sciences 9, no. 5: 909. https://doi.org/10.3390/app9050909

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop