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> How good will your deep fake have to be in order to fool a deep fake detection AI? The best generator and the best detector are actually part of the same mod
by aianus 7y ago
> How good will your deep fake have to be in order to fool a deep fake detection AI?
The best generator and the best detector are actually part of the same model! If you create a better detector, you are making the generator better at the same time so you have not accomplished anything.
"GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples."
https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/ https://machinelearningmastery.com/what-are-generative-adver...
- muttled 7y agoIs the generator always assumed to be able to keep up when the discriminator is improved algorithmically? I've always wondered that when I hear this counter-argument.
- PeterisP 7y agoWell, from the pure GAN theory perspective, it is theoretically possible for a perfect generator to exist which generates samples that perfectly match the 'source data distribution' (the only question is how do we obtain one or close to it), and thus a perfect discriminator can't exist - a fake image could be realistic and indistinguishable in all aspects. And it does seem that the human-visible weaknesses of many currently published deepfake examples are those that would be hard for the current discriminator architectures to detect, not those which would be harder to generate than any other part of the fake.