Can simpler be better? Review of methods for the detection of GAN-generated imagery

Adisree V. Ankolekar; Raaga Madappa; Andreas Savakis

doi:10.1117/12.2632737

27 May 2022 Can simpler be better? Review of methods for the detection of GAN-generated imagery

Adisree V. Ankolekar, Raaga Madappa, Andreas Savakis

Author Affiliations +

Proceedings Volume 12101, Pattern Recognition and Tracking XXXIII; 121010F (2022) https://doi.org/10.1117/12.2632737
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

As generative-adversarial-networks (GANs) continue to show progress in generating realistic imagery, there is a need to develop methods for distinguishing fake images from real images. This paper reviews state-ofthe- art methods for detecting real vs. GAN-generated images of faces. The methods used are NoiseScope, Resynthesis, Attribution Network, CNNDetector and DFT-based detection. Most methods are based on deeplearning architectures, except for the one using Discrete Fourier Transform (DFT) and a simple classifier based on azimuthal averaging of the image spectrum. While one might expect the deep-learning based methods to perform better, our initial experiments show that the DFT-based classifier performed the best and was the fastest and simplest to implement. These results illustrate that sometimes simpler methods can achieve better results, when comparing computation speed and performance, and point to the usefulness of considering a variety of approaches for the detection of fake imagery. The robustness of the methods were also assessed by adding different types of noise to the GAN generated images.

Conference Presentation

Citation Download Citation

Adisree V. Ankolekar, Raaga Madappa, and Andreas Savakis "Can simpler be better? Review of methods for the detection of GAN-generated imagery", Proc. SPIE 12101, Pattern Recognition and Tracking XXXIII, 121010F (27 May 2022); https://doi.org/10.1117/12.2632737

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