Researchers are calling into doubt the popular idea that deep-learning models are “black boxes” that reveal nothing about what goes on inside
Load up the website This Person Does Not Exist and it’ll show you a human face, near-perfect in its realism yet totally fake. Refresh and the neural network behind the site will generate another, and another, and another. The endless sequence of AI-crafted faces is produced by a generative adversarial network (GAN)—a type of AI that learns to produce realistic but fake examples of the data it is trained on.
But such generated faces—which are starting to be used in CGI movies and ads—might not be as unique as they seem. In a paper titled This Person (Probably) Exists, researchers show that many faces produced by GANs bear a striking resemblance to actual people who appear in the training data. The fake faces can effectively unmask the real faces the GAN was trained on, making it possible to expose the identity of those individuals. The work is the latest in a string of studies that call into doubt the popular idea that neural networks are “black boxes” that reveal nothing about what goes on inside.