  Last-generation GAN models allow to generate synthetic images which are
visually indistinguishable from natural ones, raising the need to develop tools
to distinguish fake and natural images thus contributing to preserve the
trustworthiness of digital images. While modern GAN models can generate very
high-quality images with no visible spatial artifacts, reconstruction of
consistent relationships among colour channels is expectedly more difficult. In
this paper, we propose a method for distinguishing GAN-generated from natural
images by exploiting inconsistencies among spectral bands, with specific focus
on the generation of synthetic face images. Specifically, we use cross-band
co-occurrence matrices, in addition to spatial co-occurrence matrices, as input
to a CNN model, which is trained to distinguish between real and synthetic
faces. The results of our experiments confirm the goodness of our approach
which outperforms a similar detection technique based on intra-band spatial
co-occurrences only. The performance gain is particularly significant with
regard to robustness against post-processing, like geometric transformations,
filtering and contrast manipulations.
