Detecting High-Quality GAN-Generated Face Images using Neural Networks
- URL: http://arxiv.org/abs/2203.01716v1
- Date: Thu, 3 Mar 2022 13:53:27 GMT
- Title: Detecting High-Quality GAN-Generated Face Images using Neural Networks
- Authors: Ehsan Nowroozi, Mauro Conti, Yassine Mekdad
- Abstract summary: We propose a new strategy to differentiate GAN-generated images from authentic images by leveraging spectral band discrepancies.
In particular, we enable the digital preservation of face images using the Cross-band co-occurrence matrix and spatial co-occurrence matrix.
We show that the performance boost is particularly significant and achieves more than 92% in different post-processing environments.
- Score: 23.388645531702597
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the past decades, the excessive use of the last-generation GAN (Generative
Adversarial Networks) models in computer vision has enabled the creation of
artificial face images that are visually indistinguishable from genuine ones.
These images are particularly used in adversarial settings to create fake
social media accounts and other fake online profiles. Such malicious activities
can negatively impact the trustworthiness of users identities. On the other
hand, the recent development of GAN models may create high-quality face images
without evidence of spatial artifacts. Therefore, reassembling uniform color
channel correlations is a challenging research problem. To face these
challenges, we need to develop efficient tools able to differentiate between
fake and authentic face images. In this chapter, we propose a new strategy to
differentiate GAN-generated images from authentic images by leveraging spectral
band discrepancies, focusing on artificial face image synthesis. In particular,
we enable the digital preservation of face images using the Cross-band
co-occurrence matrix and spatial co-occurrence matrix. Then, we implement these
techniques and feed them to a Convolutional Neural Networks (CNN) architecture
to identify the real from artificial faces. Additionally, we show that the
performance boost is particularly significant and achieves more than 92% in
different post-processing environments. Finally, we provide several research
observations demonstrating that this strategy improves a comparable detection
method based only on intra-band spatial co-occurrences.
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