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Images from ImageNet (top) alongside recreations of those images made by rewinding a model trained on ImageNet (bottom)Īs in Webster’s work, the re-created images closely resemble the real ones. In one test, they showed that they could accurately re-create images from ImageNet, one of the best known image recognition data sets. They tested the technique on a variety of common image-recognition models and GANs.
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Kautz’s team found that they could interrupt a model in the middle of these steps and reverse its direction, re-creating the input image from the internal data of the model. Each layer extracts different levels of information, from edges to shapes to more recognizable features.
#FAKE GENERATOR SERIES#
Take a trained image-recognition network: to identify what’s in an image, the network passes it through a series of layers of artificial neurons. Instead, they developed an algorithm that can re-create the data that a trained model has been exposed to by reversing the steps that the model goes through when processing that data. He and his colleagues at Nvidia have come up with a different way to expose private data, including images of faces and other objects, medical data, and more, that does not require access to training data at all. Yet this assumes that you can get hold of that training data, says Kautz. These fake faces are followed by three photos of real people identified in the training data The left-hand column in each block shows faces generated by a GAN. In many cases, the team found multiple photos of real people in the training data that appeared to match the fake faces generated by the GAN, revealing the identity of individuals the AI had been trained on. To do this, the researchers first generated faces with the GAN and then used a separate facial-recognition AI to detect whether the identity of these generated faces matched the identity of any of the faces seen in the training data. Webster’s team extended this idea so that instead of identifying the exact photos used to train a GAN, they identified photos in the GAN’s training set that were not identical but appeared to portray the same individual-in other words, faces with the same identity. For example, finding out that someone’s medical data was used to train a model associated with a disease might reveal that this person has that disease. Such attacks can lead to serious security leaks. A second, attacking model can learn to spot such tells in the first model’s behavior and use them to predict when certain data, such as a photo, is in the training set or not. These attacks typically take advantage of subtle differences between the way a model treats data it was trained on-and has thus seen thousands of times before-and unseen data.įor example, a model might identify a previously unseen image accurately, but with slightly less confidence than one it was trained on. To expose the hidden training data, Ryan Webster and his colleagues at the University of Caen Normandy in France used a type of attack called a membership attack, which can be used to find out whether certain data was used to train a neural network model.
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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. The fake faces can effectively unmask the real faces the GAN was trained on, making it possible to expose the identity of those individuals. 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 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.īut such generated faces-which are starting to be used in CGI movies and ads-might not be as unique as they seem. Refresh and the neural network behind the site will generate another, and another, and another. 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.