CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection

Takayuki Osakabe; Miki Tanaka; Yuma Kinoshita; Hitoshi Kiya

doi:10.1117/12.2590977

13 March 2021 CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection

Takayuki Osakabe, Miki Tanaka, Yuma Kinoshita, Hitoshi Kiya

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 1176609 (2021) https://doi.org/10.1117/12.2590977
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep image synthesis techniques, such as Generative Adversarial Networks (GANs) have easily generated fake images, so detecting manipulated images has become an urgent issue. Most state-of-the-art forgery detection methods assume that images include checkerboard artifacts which are generated by using DNNs. Accordingly, we propose a novel CycleGAN without any checkerboard artifacts for counter-forensics of fake-mage detection methods for the first time, as an example of GANs without checkerboard artifacts.

Citation Download Citation

Takayuki Osakabe, Miki Tanaka, Yuma Kinoshita, and Hitoshi Kiya "CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 1176609 (13 March 2021); https://doi.org/10.1117/12.2590977

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available