TY - GEN
T1 - A Scoping Review of GAN-Generated Images Detection
AU - Kit, Koh Say
AU - Wong, W. K.
AU - Chew, I. M.
AU - Juwono, Filbert H.
AU - Sivakumar, Saaveethya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The usage of Generative Adversarial Network (GAN) architectures has given anyone an ability to generate an image that is indistinguishable from the real image. The improper use of GAN-generated images may lead to serious privacy, security, political, and social consequences such as spreading of fake information and legal issue. Therefore, it is crucial to emphasize the widespread of fake imaginary by developing a fake image detection system. Convolutional Neural Network (CNN) is traditional method in detecting GAN-generated images. However, due to the advancement and variations of GAN, CNN often suffer from limited generalization. Benford's law can also be applied to produce features that can be used to detect GAN-generated images. In this paper, the fundamentals of GAN, and the technology used in fake image detection model will be discussed and reviewed thoroughly.
AB - The usage of Generative Adversarial Network (GAN) architectures has given anyone an ability to generate an image that is indistinguishable from the real image. The improper use of GAN-generated images may lead to serious privacy, security, political, and social consequences such as spreading of fake information and legal issue. Therefore, it is crucial to emphasize the widespread of fake imaginary by developing a fake image detection system. Convolutional Neural Network (CNN) is traditional method in detecting GAN-generated images. However, due to the advancement and variations of GAN, CNN often suffer from limited generalization. Benford's law can also be applied to produce features that can be used to detect GAN-generated images. In this paper, the fundamentals of GAN, and the technology used in fake image detection model will be discussed and reviewed thoroughly.
KW - Benford's law
KW - CNN
KW - GAN
UR - http://www.scopus.com/inward/record.url?scp=85173631007&partnerID=8YFLogxK
U2 - 10.1109/ICDATE58146.2023.10248679
DO - 10.1109/ICDATE58146.2023.10248679
M3 - Conference Proceeding
AN - SCOPUS:85173631007
T3 - 2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023
BT - 2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023
Y2 - 14 July 2023 through 16 July 2023
ER -