@Article{jihpp.2020.09839,
AUTHOR = {Xin Liu, Xiao Chen},
TITLE = {A Survey of GAN-Generated Fake Faces Detection Method Based on Deep  Learning},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {87--94},
URL = {http://www.techscience.com/jihpp/v2n2/40538},
ISSN = {2637-4226},
ABSTRACT = {In recent years, with the rapid growth of generative adversarial 
networks (GANs), a photo-realistic face can be easily generated from a random 
vector. Moreover, the faces generated by advanced GANs are very realistic. It is 
reasonable to acknowledge that even a well-trained viewer has difficulties to 
distinguish artificial from real faces. Therefore, detecting the face generated by 
GANs is a necessary work. This paper mainly introduces some methods to detect 
GAN-generated fake faces, and analyzes the advantages and disadvantages of 
these models based on the network structure and evaluation indexes, and the 
results obtained in the respective data sets. On this basis, the challenges faced in 
this field and future research directions are discussed.},
DOI = {10.32604/jihpp.2020.09839}
}