@Article{jihpp.2020.010462,
AUTHOR = {Jiawei Zhang, Jinwei Wang},
TITLE = {A Survey on Adversarial Example},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {47--57},
URL = {http://www.techscience.com/jihpp/v2n1/40335},
ISSN = {2637-4226},
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.},
DOI = {10.32604/jihpp.2020.010462}
}