![]() 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. 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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