The first was an entirely new type of DNN, the generative adversarial network (GAN) created in 2014. The transition from facial recognition and image classification to facial reenactment and swapping occurred when researchers within the same field began using additional types of DNN models. The DeepId tool expanded on this work, tweaking the CNNs in various ways. One of the main works to do so, DeepFace, used a deep convolutional neural network (CNN) to classify a set of 4 million human images. While it used machine learning that was common in the computer-vision field at the time, it did not use DNNs, and hence a video it produced would not be considered a deepfake.Ĭomputer-vision research using machine learning continued throughout the 2000s, and in the mid-2010s, the first academic works using DNNs to perform face recognition emerged. In 1997 researchers working on lip-syncing created the Video Rewrite program, which could create a new video from existing footage of a person saying something different than what was in the original clip.
In time, this forum was banned by Reddit, but the technology had become popular, and its implications for privacy and identity fraud became apparent.Īlthough the term originated in late 2017, the technology of using machine learning in the field of computer-vision research was well established in the film and videogame industries and in academia. These videos were pornographic, and after the user created a forum for them, r/deepfakes, it attracted many members, and the technology spread through the amateur world. The term deepfake originated from the screen name of a member of a popular Reddit forum who in 2017 first posted deepfaked videos. This type is also called a “puppet-master” scenario because the identity of the puppet (destination) is preserved, while his or her expressions are driven by a master (source). In a reenactment video, a source person drives the facial expressions and head movements of a destination person, preserving the identity of the destination. The destination’s facial expressions and head movements remain the same, but the identity takes on that of the source. In a replacement, also called a “faceswap,” the identity of a source subject is transferred onto a destination subject’s face. For videos, identities can be substituted in two ways: replacement or reenactment. There are numerous DNN architectures used in deep learning that are specialized for image, video, or speech processing. The Evolution of Deepfake TechnologyĪ DNN is a neural network that has more than one hidden layer. What’s more, as the idea of deepfakes has gained visibility in popular media, the press, and social media, a parallel threat has emerged from the so-called liar’s dividend-challenging the authenticity or veracity of legitimate information through a false claim that something is a deepfake even if it isn’t. However, the existence of a wide range of video-manipulation tools means that video discovered online can’t always be trusted. As of February 2020, Internet users were uploading an average of 500 hours of new video content per minute on YouTube alone. The large volume of online video presents an opportunity for the United States Government to enhance its situational awareness on a global scale.
In this blog post, I describe the technology underlying the creation and detection of deepfakes and assess current and future threat levels. The House Intelligence Committee discussed at length the rising risks presented by deepfakes in a public hearing on June 13, 2019.
Create deep fake software#
Evolutionary improvements in video-generation methods are enabling relatively low-budget adversaries to use off-the-shelf machine-learning software to generate fake content with increasing scale and realism. A report published this year estimated that there were more than 85,000 harmful deepfake videos detected up to December 2020, with the number doubling every six months since observations began in December 2018.ĭetermining the authenticity of video content can be an urgent priority when a video pertains to national-security concerns. The destination’s facial expressions and head movements remain the same, but the appearance in the video is that of the source. This alteration typically takes the form of a “faceswap” where the identity of a source subject is transferred onto a destination subject. A deepfake is a media file-image, video, or speech, typically representing a human subject-that has been altered deceptively using deep neural networks (DNNs) to alter a person’s identity.
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