  Generative Adversarial Networks (GANs) have paved the path towards entirely
new media generation capabilities at the forefront of image, video, and audio
synthesis. However, they can also be misused and abused to fabricate elaborate
lies, capable of stirring up the public debate. The threat posed by GANs has
sparked the need to discern between genuine content and fabricated one.
Previous studies have tackled this task by using classical machine learning
techniques, such as k-nearest neighbours and eigenfaces, which unfortunately
did not prove very effective. Subsequent methods have focused on leveraging on
frequency decompositions, i.e., discrete cosine transform, wavelets, and
wavelet packets, to preprocess the input features for classifiers. However,
existing approaches only rely on isotropic transformations. We argue that,
since GANs primarily utilize isotropic convolutions to generate their output,
they leave clear traces, their fingerprint, in the coefficient distribution on
sub-bands extracted by anisotropic transformations. We employ the fully
separable wavelet transform and multiwavelets to obtain the anisotropic
features to feed to standard CNN classifiers. Lastly, we find the fully
separable transform capable of improving the state-of-the-art.
