A One-Class Classifier for the Detection of GAN Manipulated
  Multi-Spectral Satellite Images
Lydia Abady Giovanna Maria Dimitri Mauro Barni
The highly realistic image quality achieved by current image generative
models has many academic and industrial applications. To limit the use of such
models to benign applications, though, it is necessary that tools to
conclusively detect whether an image has been generated synthetically or not
are developed. For this reason, several detectors have been developed providing
excellent performance in computer vision applications, however, they can not be
applied as they are to multispectral satellite images, and hence new models
must be trained. In general, two-class classifiers can achieve very good
detection accuracies, however they are not able to generalise to image domains
and generative models architectures different than those used during training.
For this reason, in this paper, we propose a one-class classifier based on
Vector Quantized Variational Autoencoder 2 (VQ-VAE 2) features to overcome the
limitations of two-class classifiers. First, we emphasize the generalization
problem that binary classifiers suffer from by training and testing an
EfficientNet-B4 architecture on multiple multispectral datasets. Then we show
that, since the VQ-VAE 2 based classifier is trained only on pristine images,
it is able to detect images belonging to different domains and generated by
architectures that have not been used during training. Last, we compare the two
classifiers head-to-head on the same generated datasets, highlighting the
superiori generalization capabilities of the VQ-VAE 2-based detector.