Preprint arXiv:arXiv:2004.07682 [cs.CV]; last accessed April 21, 2020 (2020 25th International Conference on Pattern Recognition (ICPR), pp. 5495-5502) .
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.
Bibtex:
@misc{,
title={On the use of Benford's law to detect GAN-generated images},
author={Nicolò Bonettini and Paolo Bestagini and Simone Milani and Stefano Tubaro},
year={2020},
eprint={2004.07682},
archivePrefix={arXiv},
primaryClass={cs.CV}
url={ https://arxiv.org/abs/2004.07682},
}
Reference Type: Preprint
Subject Area(s): Accounting, Computer Science, Image Processing, Social Sciences