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Why not both? Generative Adversarial Networks http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ http://blog.aylien.com/intro

• PeterisP 8y ago
  The particular videos are designed by this approach (GANs), and thus the generator is highly adapted to generate data that can't be distinguished from a real video with these techniques. If it's built with state-of-art GANs, then you don't be able to reliably detect it with discriminators from these state-of-art GANs. If you manage to build a better automatic discriminator than the authors did, then they could directly use it in the adversarial approach to build a better generator, and your detection approach would again cease functioning. Remember that in the end there's a fundamental asymmetry - in theory, there could be a perfect generator (not that we're close to perfect yet) that generates undistinguishable samples, and thus there can not be a perfect discriminator.