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Generative Adversarial Networks (GANs) have recently achieved unprecedentedsuccess in photo-realistic image synthesis from low-dimensional random noise.The ability to synthesize high-quality content at a large scale bringspotential risks as the generated samples may lead to misinformation that cancreate severe social, political, health, and business hazards. We proposeSubsetGAN to identify generated content by detecting a subset of anomalousnode-activations in the inner layers of pre-trained neural networks. Thesenodes, as a group, maximize a non-parametric measure of divergence away fromthe expected distribution of activations created from real data. This enable usto identify synthesised images without prior knowledge of their distribution.SubsetGAN efficiently scores subsets of nodes and returns the group of nodeswithin the pre-trained classifier that contributed to the maximum score. Theclassifier can be a general fake classifier trained over samples from multiplesources or the discriminator network from different GANs. Our approach showsconsistently higher detection power than existing detection methods acrossseveral state-of-the-art GANs (PGGAN, StarGAN, and CycleGAN) and over differentproportions of generated content.

Pattern Detection in the Activation Space for Identifying Synthesized Content