“You can’t do it by just pressing a button,” Ume told The Verge in a recent interview. He compared the current deepfake technology to that of Photoshop, telling host Alex Hern that, just as it takes professional-level skills to create superior images with Photoshop, you need a high level of skill and experience for generating undetectable deepfakes. In addition to massive amounts of pre-training data in the form of random faces and the training data required for Cruise’s face specifically, Ume attributes the authenticity of his deepfakes to the performance of professional actor Miles Fisher.įisher has perfected Cruise’s gestures, mannerisms and facial expressions, which provided the destination videos that Ume used in his deepfakes. Recalling Ume’s explanation of the resources needed to make realistic synthetic media, we can deduce that some methods are more precise and exacting than others. This includes altering expressions, swapping the faces of two real people or generating a nonexistent human face from a dataset that includes thousands of images of real people. For a piece of synthetic media to qualify as a true deepfake, it must use deep-learning training techniques to achieve the goal of facial manipulation. There are several ways to make deepfake videos. For now, the key takeaway is that deepfake technology, which is based on deep-learning models, has been around for decades.ĭeep learning has its roots in cognitive science and has been advanced over the years by researchers in diverse fields, including computer science, artificial intelligence, neurophysiology, cybernetics and logic. We’ll get into a discussion of data and machine-learning training techniques in the section on creating a deepfake. “The important thing is you cover all angles. You have as much expression as possible and even try to have a lot of different light angles, so the machine knows how Tom’s face reacts in certain scenes,” said Ume. The actor has been filmed and photographed for nearly 40 years, so the sheer volume of data that could be used for training makes the output - i.e., the deepfake - a stunningly accurate representation. This abundance of available data is a big part of what makes the Tom Cruise videos so uncannily authentic. And then you scrub through them and you clean it up so you only have the best of the best.” In an interview with Science Weekly, Ume explained that such sophisticated deepfakes require “as much data as possible - pictures, videos, anything you can find. Similar to the way neurons in the human brain create meaning as they process the data they receive, artificial neural networks, or ANNs, pass raw data (noise) from their input layers to their middle (hidden) layers and finally to the output layer.Īs we’ll see when we get to the section on how to create a deepfake video, image, or audio by way of artificial intelligence deep-learning models, the most accurate synthetic media outputs are those that result from a large volume of high-quality data.įor example, some of the most popular deepfakes have come from visual effects specialist Chris Ume, who offered a peek behind the curtain of his unsettlingly realistic viral deepfakes of Tom Cruise on TikTok. Hinton’s artificial neural network, an integral component of advanced deepfake techniques used today, was intended to closely resemble the architecture of the human brain, relaying signals through layers of nodes that process large amounts of data to learn and classify information. This, however, is not where the deepfake story begins, ends, or best succeeds.ĭeep-learning technology, including rudimentary versions of the models that make deepfakes - also known as synthetic media - has existed for decades, but the limited graphics processing power of computers at that time made most applications cumbersome and impractical.Īccording to freeCodeCamp contributor Nick McCullum, the cognitive psychologist and computer scientist Geoffrey Hinton contributed significantly to the study of deep learning with his introduction of the artificial neural network. Though cropping up in a variety of applications, Deepfakes have been implemented within the porn industry more than any other to date. A 2019 report released by Amsterdam-based cybersecurity firm Sensity - formerly Deeptrace - found that “nonconsensual deepfake pornography accounted for 96% of the total deepfake videos online.” In 2017 a Reddit user by the name of “deepfake” posted pornographic videos created through the use of face-swapping technology that replaced the original subjects’ faces with those of known celebrities. Sponsored Content A Brief History of Deepfake Technology
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