AI-Manipulated Content Detection: Challenging Arms Race

Technical University of Denmark

Nude photos of pop superstar Taylor Swift recently flooded social media X, where they were viewed and shared by millions of users. But the images weren't real - they were deepfakes created using artificial intelligence (AI). The incident fanned the debate about regulating deepfakes, and drew so much attention that the White House got involved. Solutions are now being discussed among politicians and big tech companies. But is it even possible to guard against this kind of attack? According to Morten Mørup, who does research in artificial intelligence at DTU Compute, it could be a very difficult task.

Deepfake uses deep learning to create text, images, speech, or video that are presented as being real but are far from it. The development of deepfakes took off in 2014 when the Generative Adversarial Network (GAN) AI learning principle was developed. The principle allows AI models to train against other AI models designed to detect deepfakes. Since then, AI models have been developed that make it even harder to tell the difference between fake and reality, and there are many deepfake tools available on the Internet.

"The GAN learning principle is based on an arms race between two AI models: one that generates deepfakes, and another that tries to distinguish between what is real and AI-created. The deepfake models test themselves against models designed to detect them. If the AI model designed to produce deepfakes is trained against another AI model designed to determine whether, say, an image is real or fake, it will learn from this AI model how it needs to improve. This race between two AI models - one trying to generate and the other to detect fake material - will continue until the detection model can no longer distinguish between reality and fake. This is what makes it so difficult for both people and AI models to tell the difference between what is real and fake," says Morten Mørup.

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