NVIDIA-Powered Neural Network Produces Freakishly Natural Fake Human Photos (hothardware.com)

MojoKid writes: NVIDIA released a paper recently detailing a new machine learning methodology for generating unique and realistic looking faces using a generative adversarial network (GAN). The result is the ability to artificially render photorealistic human faces of "unprecedented quality." NVIDIA achieves this by using an algorithm that pairs two neural networks -- a generator and a discriminator -- that compete against each other. The generator starts from a low resolution image and builds upon it, while the discriminator assesses the results, sort of like a constant critic, pointing out where things have gone wrong. The GAN is not a new technology, but where NVIDIA differentiates is through the progressive training method it developed. NVIDIA took a database of photographs of famous people and used that to train its system. By working together, the neural networks were able to produce fake images that are nearly indistinguishable from real human photographs, and a little creepy too.

Comments

Curriculum learning

[LetterRip]

This sounds like the standard idea of curriculum learning - you teach NNs via progressively more difficult tasks.

Re:Curriculum learning

[LetterRip]

It isn't in GANs one model, the generative, tries to fool the other, classifier one, by giving it images itself has generated. There's no incremental task switching from easier problem domains to more difficult ones.

I'm familiar with GANs - what it sounded like (and what they did) is add curriculum learning, but they also did it layerwise as is done with autoencoders, (Also they had some other interesting ideas, but that was the crucial bit). In this case the easy is the lower resolution images and the hard is the higher dimensional images.

From their paper

The idea of growing GANs progressively is related to curriculum GANs (Anonymous), where the idea is to attach multiple discriminators that operate on different spatial resolutions to a single generator, and furthermore adjust the balance between resolutions as a function of training time. That work in turn is motivated by Durugkar et al. (2016) who use one generator and multiple discriminators concurrently, and Ghosh et al. (2017) who do the opposite with multiple generators and one discriminator. In contrast to early work on adaptively growing networks, e.g., growing neural gas (Fritzke, 1995) and neuro evolution of augmenting topologies (Stanley & Miikkulainen, 2002) that grow networks greedily, we simply defer the introduction of pre-configured layers. In that sense our approach resembles layer-wise training of autoencoders (Bengio et al., 2007).

Re:Training database seems skewed

[BradleyUffner]

The rendered images look strikingly like actual human photographs, I'll bet they could fool nearly everyone -- you'd have to have a reason to think they were fake.

I'm wondering if their choice of celebrities as the training database somehow skews their results positive versus "ordinary" people. Celebrities almost seem too uniform in terms of facial features and general appearance. It makes me wonder if they tried with ordinary people if the algorithm woudln't produce freaks because it sees odd deviations among normal people.

If you look at the full paper, this is capable of so much more than faces. There are dozens of pages of every-day objects they generated, from bedrooms, to wine bottles, to boats, and bicycles. A few of them of some pretty obvious warping and distortions, but the ones that don't look like real objects. It's mind blowing.

Re:Can the criminal system keep up?

[nealric]

Speaking as a lawyer, I'm afraid you have far too much confidence in the judicial system. People have been convicted based on a lot less than a seemingly perfect photograph and few criminal defendants have the financial wherewithal to hire an expert to contest the veracity of a spoofed photo.

Re:Not Bad

[Anonymous Coward]

Already happened in Japan - Hatsune Miku virtual singer

https://www.wmagazine.com/story/hatsune-miku-crypton-future