The Future of Generative Adversarial Networks in Deepfakes

ai deepfake

Excluding ‘traditional’ CGI methods, which date back to the 1970s, there are currently three mainstream AI-based approaches to creating synthetic human faces, only one of which has attained any widespread success or societal impact: autoencoder frameworks (the architecture behind current viral deepfakes); Generative Adversarial Networks (GANs); and Neural Radiance Fields (NeRF).

Of these, NeRF — a late entrant that’s also capable of recreating the entire human form — is at the most rudimentary stage in terms of its facial generation capabilities; GANs can create the most convincing faces, but are still too volatile and ungovernable to easily output realistic video footage; and autoencoder frameworks, which have captivated (and, arguably, menaced) the world, require ‘host’ footage, and are largely confined to the inner areas of the face, which adds the further burden of finding a ‘target’ who closely resembles the ‘injected’ identity.

Last time, we took a look at the challenges facing NeRF as a future contender for the deepfake crown; in the next article, we’ll examine how the most popular current autoencoder-based deepfake approaches work, and whether they can maintain a vanguard position in face replacement.

For now, let’s see where Generative Adversarial Networks, among the most celebrated image synthesis techniques of the last five years, might fit into the future of deepfakes.

Combat Training

During training, a Generative Adversarial Network extracts high-level features from thousands of images in order to develop the capacity to reproduce similar images in the same domain as the dataset (i.e. ‘faces’, ‘cars’, ‘churches’, etc.).