  Generative Adversarial Networks (GANs) have recently achieved unprecedented
success in photo-realistic image synthesis from low-dimensional random noise.
The ability to synthesize high-quality content at a large scale brings
potential risks as the generated samples may lead to misinformation that can
create severe social, political, health, and business hazards. We propose
SubsetGAN to identify generated content by detecting a subset of anomalous
node-activations in the inner layers of pre-trained neural networks. These
nodes, as a group, maximize a non-parametric measure of divergence away from
the expected distribution of activations created from real data. This enable us
to identify synthesised images without prior knowledge of their distribution.
SubsetGAN efficiently scores subsets of nodes and returns the group of nodes
within the pre-trained classifier that contributed to the maximum score. The
classifier can be a general fake classifier trained over samples from multiple
sources or the discriminator network from different GANs. Our approach shows
consistently higher detection power than existing detection methods across
several state-of-the-art GANs (PGGAN, StarGAN, and CycleGAN) and over different
proportions of generated content.
