Deepfakes are often based on generative adversarial networks (Gan), where a generator and a discriminator try to outcompete each other. ‘One network tries to generate a fake image from white noise, let’s say a face,’ explains deepfake technology researcher John (Saniat) Sohrawardi from the Rochester Institute of Technology, US. ‘It doesn’t know how to generate a face initially, so it takes the help of a discriminator, which is another network that learns how to tell apart whether an image is real or fake.’ Eventually, the generator will fool the discriminator into thinking its images are real.
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