Exploring the power of generative adversarial networks

The Art of Forgery

© Lead Image © Aliaksandr-Marko, 123RF.com

© Lead Image © Aliaksandr-Marko, 123RF.com

Article from Issue 266/2023

Auction houses are selling AI-based artwork that looks like it came from the grand masters. The Internet is peppered with photos of people who don't exist, and the movie industry dreams of resurrecting dead stars. Enter the world of generative adversarial networks.

Machine learning models that could recognize objects in images – and even create entirely new images – were once no more than a pipe dream. Although the AI world discussed various strategies, a satisfactory solution proved illusive. Then in 2014, after an animated discussion in a Montreal bar, Ian Goodfellow came up with a bright idea.

At a fellow student's doctoral party, Goodfellow and his colleagues were discussing a project that involved mathematically determining everything that makes up a photograph. Their idea was to feed this information into a machine so that it could create its own images. At first, Goodfellow declared that it would never work. After all, there were too many parameters to consider, and it would be hard to include them all. But back home, the problem was still on Goodfellow's mind, and he actually found the solution that same night: Neural networks could teach a computer to create realistic photos.

His plan required two networks, the generator and the discriminator, interacting as counterparts. The best way to understand this idea is to consider an analogy. On one side is an art forger (generator). The art forger wants to, say, paint a picture in the style of Vincent van Gogh in order to sell it as an original to an auction house. On the other hand, an art detective and a real van Gogh connoisseur at the auction house try to identify forgeries. At first, the art expert is quite inexperienced, but the detective immediately recognizes that it is not a real van Gogh. Nevertheless, the counterfeiter does not even think of giving up. The forger keep practicing and trying to foist new and better paintings off on the detective. In each round, the painting looks more like an original by a famous painter, until the detective finally classifies it as genuine.


Use Express-Checkout link below to read the full article (PDF).

Buy this article as PDF

Express-Checkout as PDF
Price $2.95
(incl. VAT)

Buy Linux Magazine

Get it on Google Play

US / Canada

Get it on Google Play

UK / Australia

Related content

  • Machine Learning

    "I won't make this mistake again," you promise yourself. In other words, you'll learn from experience. If you translate experience into data, computers can do that, too. We'll introduce you to the fundamental forms of machine learning.

  • GameHub

    If you regularly buy games through Steam, GOG, and Humble Bundle, GameHub can help you keep them organized by bringing them all together into a single library.

  • Disinformation Detector

    Fake information is experiencing a boom, but given the right tools, you can reliably separate the wheat from the chaff.

  • ASCII Art

    Creating images from letters and numbers is a complex matter, unless you have the right tools at hand.

  • FOSSPicks

    This month Graham looks at Cecilia 5, chezmoi, Viddy, EmuDeck, Paperless-ngx, MegaGlest, and more!

comments powered by Disqus

Direct Download

Read full article as PDF:

Price $2.95

Subscribe to our Linux Newsletters
Find Linux and Open Source Jobs
Subscribe to our ADMIN Newsletters
Find SysAdmin Jobs