![]() ![]() Why It Matters: Deepfake videos are a growing threat. Then, using deep learning, we can instantly detect whether a video is real or fake. These blood flow signals are collected from all over the face and algorithms translate these signals into spatiotemporal maps. When our hearts pump blood, our veins change color. In contrast, FakeCatcher looks for authentic clues in real videos, by assessing what makes us human- subtle “blood flow” in the pixels of a video. Most deep learning-based detectors look at raw data to try to find signs of inauthenticity and identify what is wrong with a video. On the hardware side, the real-time detection platform can run up to 72 different detection streams simultaneously on 3rd Gen Intel® Xeon® Scalable processors. ![]() Teams also leaned on the Open Visual Cloud project to provide an integrated software stack for the Intel® Xeon® Scalable processor family. Computer vision blocks were optimized with Intel® Integrated Performance Primitives (a multi-threaded software library) and OpenCV (a toolkit for processing real-time images and videos), while inference blocks were optimized with Intel® Deep Learning Boost and with Intel® Advanced Vector Extensions 512, and media blocks were optimized with Intel® Advanced Vector Extensions 2. Teams used OpenVino™ to run AI models for face and landmark detection algorithms. Using Intel hardware and software, it runs on a server and interfaces through a web-based platform. On the software side, an orchestra of specialist tools form the optimized FakeCatcher architecture. The Organization for Social Media Safety has sponsored AB 1280 in the California State Assembly which will deter the production of deepfakes that misappropriate identities for pornographic videos by creating criminal liability for those that create and distribute deepfakes for these purposes.How it Works: Intel’s real-time platform uses FakeCatcher, a detector designed by Demir in collaboration with Umur Ciftci from the State University of New York at Binghamton. With the technology improving and becoming more accessible, this disturbing trend has expanded outside of Hollywood and into lives of everyday Californians, with the most likely targets including the most vulnerable among us, such as survivors of abusive relationships and minors. Victims are left with emotional trauma, severe mental anguish, and reputational damage. ![]() Since their introduction, deepfake technology has been utilized extensively to create fake pornographic videos using the likenesses of female celebrities, without consent. ![]() While deepfakes do have a number of beneficial uses, including political satire, comedy, entertainment, and education, a number of its associated dangers are severe, even existential threats. Other users then shared the code on GitHub, a major code sharing service, where it became free software and publicly available. Applications, like FakeApp, soon appeared simplifying the programming process. Deepfakes refer to forged or fake videos created via deep learning, a form of artificial intelligence, where a person’s likeness, including their face and voice, can be realistically swapped with someone else’s.ĭeepfake technology first appeared in November 2017 when an anonymous user on the social media platform Reddit posted an algorithm that leveraged existing artificial intelligence algorithms to create realistic fake videos. ![]()
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