![]() Companies will spend up to $188 billion in cybersecurity solutions, according to Gartner. 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. 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. ![]()
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