Deepfake detection tools must generally be trained with large and diverse data sets to reliably detect deepfakes. Speech synthesis and facial manipulation can make it appear that a person is authentically speaking another language. Retailers could let customers use their likenesses to virtually try on clothing.
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Voices and likenesses can be used in a movie to achieve a creative effect or maintain a cohesive story when the entertainers themselves are not available. There are some potentially benign or beneficial uses of this technology: As artificial neural network technologies have advanced rapidly in parallel with more powerful and abundant computing, so has the ability to produce realistic deepfakes. More convincing deepfakes created with GANs require more advanced technical skills and resources. However, to develop a somewhat realistic deepfake, these applications generally still require hundreds or thousands of training images of the faces to be swapped or manipulated, making celebrities and government leaders the most common subjects.
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Computer applications are openly available on the internet with tutorials on how to create deepfake videos. How mature is it? Anyone with basic computer skills and a home computer can create a deepfake. These methods typically also use AI to analyze videos for digital artifacts or details that deepfakes fail to imitate realistically, such as blinking or facial tics. Researchers and internet companies have experimented with several methods to detect deepfakes. GANs generally produce more convincing deepfakes but are more difficult to use. This competition continues over many cycles, resulting in a more plausible rendering of, for example, faces in video. A GAN is made up of two competing artificial neural networks, one trying to produce a fake, the other trying to detect it. An autoencoder is an artificial neural network trained to reconstruct input from a simpler representation. Developing a deepfake photo or video typically involves feeding hundreds or thousands of images into the artificial neural network, “training” it to identify and reconstruct patterns-usually faces.ĭeepfakes use different underlying AI technologies-notably autoencoders and generative adversarial networks (GANs). How does it work? Deepfakes rely on artificial neural networks, which are computer systems modeled loosely on the human brain that recognize patterns in data. They could also lead to disregard of legitimate evidence of wrongdoing and, more generally, undermine public trust in audiovisual content. Deepfakes could be used to influence elections or incite civil unrest, or as a weapon of psychological warfare. Further, there is concern about potential growth in the use of deepfakes for other purposes, particularly disinformation. According to a recent report from the company Deeptrace, much of deepfake content online is pornographic, and deepfake pornography disproportionately victimizes women. While deepfakes have benign and legitimate applications in areas such as entertainment and commerce, they are commonly used for exploitation. In facial manipulation, the expressions of the face on the left are imitated by the face on the right. In face swapping, the face on the left is placed on another person’s body. Deepfake videos commonly swap faces or manipulate facial expressions. Deepfakes can depict someone appearing to say or do something that they in fact never said or did.įigure 1. The underlying technology can replace faces, manipulate facial expressions, synthesize faces, and synthesize speech. What is it? A deepfake is a video, photo, or audio recording that seems real but has been manipulated with AI. The underlying artificial intelligence (AI) technologies are widely available at low cost, and improvements are making deepfakes harder to detect. Deepfakes could influence elections and erode trust but so far have mainly been used for non-consensual pornography.
Deepfakes are powerful tools that can be used for exploitation and disinformation.
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