Training a supervised model can produce high-quality results, but requires data pairing. This challenge is to minimize the amount of training data required to produce quality images and to enable the execution of trained models on new identities (unseen during training). High-quality deepfakes are often achieved by training on hours of footage of the target. The survey identifies that researchers have been focusing on resolving the following challenges of deepfake creation: To demonstrate the threat, the authors successfully performed the attack on a hospital in a White hat penetration test.ДЄ survey of deepfakes, published in May 2020, provides a timeline of how the creation and detection deepfakes have advanced over the last few years. The result was so convincing that it fooled three radiologists and a state-of-the-art lung cancer detection AI. In this work, it was shown how an attacker can automatically inject or remove lung cancer in a patient's 3D CT scan.

Researchers have also shown that deepfakes are expanding into other domains such as tampering medical imagery. This project expands the application of deepfakes to the entire body previous works focused on the head or parts of the face.

In August 2018, researchers at the University of California, Berkeley published a paper introducing a fake dancing app that can create the impression of masterful dancing ability using AI. The project lists as a main research contribution the first method for re-enacting facial expressions in real time using a camera that does not capture depth, making it possible for the technique to be performed using common consumer cameras. The Face2Face program, published in 2016, modifies video footage of a person's face to depict them mimicking the facial expressions of another person in real time. The project lists as a main research contribution its photorealistic technique for synthesizing mouth shapes from audio. The 'Synthesizing Obama' program, published in 2017, modifies video footage of former president Barack Obama to depict him mouthing the words contained in a separate audio track. Д¬ontemporary academic projects have focused on creating more realistic videos and on improving techniques. It was the first system to fully automate this kind of facial reanimation, and it did so using machine learning techniques to make connections between the sounds produced by a video's subject and the shape of the subject's face. An early landmark project was the Video Rewrite program, published in 1997, which modified existing video footage of a person speaking to depict that person mouthing the words contained in a different audio track. Academic research ДЄcademic research related to deepfakes lies predominantly within the field of computer vision, a subfield of computer science. More recently the methods have been adopted by industry. Technology steadily improved during the 20th century, and more quickly with digital video.Дeepfake technology has been developed by researchers at academic institutions beginning in the 1990s, and later by amateurs in online communities. Photo manipulation was developed in the 19th century and soon applied to motion pictures.

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