Thus, the generated image is a simulated image that appears to depict the subject of the reference image, but it is not actually a real image.Īs used in the patent, a real image refers to a photographic image of a person that represents the person as they appeared at the time that the image was captured.Īs explained in the patent, the systems and methods described first modify a shape description for the subject's face according to a change in facial expression and a change in pose. The synthetic images can incorporate changes in facial expression and pose.Īt inference time, a single reference image can generate an image that looks like the person (i.e., the subject) of the reference image, but shows the face of the subject according to an expression and/or pose that the system or method has not previously seen. More on that later.Īpple's newly granted patent notes that their invention covers systems and methods that relate to the creation of synthetic images of human faces based on a reference image. In fact, Apple already has the technology in place.
The patent is titled "Face Image Generation with Pose and Expression Control." Of course, it's not as sophisticated as the TV series or the Obama presentation at present, but it definitely illustrates how Apple thinks this could be a future photos manipulation feature and/or app for still photos and videos. Patent Office granted Apple a patent for just that: Deepfake creations or Synthetic creations. So, what does this have to do with Apple? Well, yesterday the U.S. One could only image would could be done four years later. To back-up this concept, below is a video that is perhaps more convincing of the creation of a deepfake that is closer to reality. Last weekend I stumbled on the Amazon Prime Mystery Thriller series called "The Capture" (second season). This has elicited responses from both industry and government to detect and limit their use. The main machine learning methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders, or generative adversarial networks (GANs).ĭeepfakes have garnered widespread attention for their potential use in creating celebrity pornographic videos, fake news, hoaxes, bullying, and financial fraud. While the act of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content that can more easily deceive. According to Wikipedia, Deepfakes are synthetic media in which a person in an existing image or video is replaced with someone else's likeness.
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