Given a large dataset of images from a variety of angles and facial expressions, a VAE can be trained to encode parts of a face that are important (e.g.
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The decoder learns how to convert this abstract representation back into an image of the original input object. The encoder reduces an input image into a lower-dimensional representation called the latent space.
They are split into two parts called the encoder and the decoder. VAEs are a type of neural network used to create Deepfake videos (3). Within this group, variational autoencoders (VAEs) and generative adversarial networks (GANs) have performed best at learning how to produce images of almost any object. Technologies that have proven most effective at video and audio manipulation fall into the category of neural networks known as deep generative models.
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Cat or Dog) or transform a series of input numbers into a format mathematically identical to the pixels in an image. Neural networks are algorithms that can perform many tasks including learning to classify images into some category (e.g. The technologies behind AI-assisted disinformation With high apparent public awareness and a paucity of impactful politically-motivated Deepfakes so far, are the concerns of scholars like Lin justified? Just how good will these tools get, and what can be done to limit their negative impacts? Additionally, some of the most viral Deepfakes are educational in nature, produced to warn the public of the danger of these technologies (see, for example, Buzzfeed’s potty-mouthed Obama or Sassy Justice’s Deepfake Trump as an investigative reporter). Indeed, according to research from the Deepfake monitoring platform Sensity, the majority of Deepfakes produced thus far have no political valence and are made purely for entertainment (including a large NSFW genre) (2). These technologies would serve as a powerful vector for disinformation-falsehoods deliberately created to harm individuals or organisations.ĭespite these dire warnings, Deepfakes-videos that use deep learning algorithms to manipulate the identities of people featured in them-do not appear to be corroding the fabric of society just yet. A key development, Lin stressed, was that of highly accessible AI-assisted “Deepfake” technologies that allow almost anyone to create realistic forgeries at low cost. If unmet, these forces were poised to usher in “a global information dystopia”, paralysing governments as waves of conspiracies fracture citizens into information siloes. Writing in the June 2019 issue of the Bulletin, Herbert Lin, a research fellow at the Center for International Security and Cooperation, proposed a new existential risk he described as “cyber-enabled information warfare” (1).įar from simply acting as a force-multiplier for the existing risks to the species, Lin claimed that emerging information technologies posed a distinct threat in their own right, with the power to shatter the pillars of modern democratic governments: logic, truth and reality. Founded by biophysicists who had participated in the Manhattan Project, the Bulletin aims to provide the public with reliable information about technological developments that endanger the human species, historically focussing on the risks of nuclear war. The Bulletin of the Atomic Scientists is one of the oldest organisations dedicated to raising awareness of existential risk. Author: Shaan Subramaniam Editor: Gregory Milne Editor-in-Chief: Chandan Seth Nanda
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