Are Deepfakes Too Deep for Us? Or Can We Fight Back?

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Since 2014, there has been a new twist to misrepresentation in politics: deepfakes—computer-generated images that seem quite real. Adam Garfinkle of Singapore’s Nanyang Technological University explains how the technology, generative adversarial networks (GANs), works:

A GAN operator pits a generator (G) against a discriminator (D) in a gamelike environment in which G tries to fool D into incorrectly discriminating between fake and real data. The technology works by means of a series of incremental but rapid adjustments that allows D to discriminate data while G tries to fool it.Adam Garfinkle, ““Disinformed”” at Inference Review

Once the problem is reduced to a giant calculation, a giant computer learns much more quickly than the rest of us. And it can then be turned around to generate convincing fakes.

Despite what we might fear, most uses of deepfakes are not attempts to deceive:

GANs can reconstruct three-dimensional images from two-dimensional photographs. They can be used to visualize industrial design, improve astronomical images by filling in statistically what real cameras cannot capture, and generate showers of imaginary particles for high-energy physics experiments. GANs can also be used to visualize motion in static environments, which could help find people lost or hiding in …

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