"Intel's New Deepfake Detector Can Spot a Real or Fake Video Based on Blood Flow in Video Pixels"
Deepfakes have proliferated on the Internet in recent years. Deepfakes are pieces of fabricated media that use an image or video and someone else's face or voice to create a new, fictitious image of people or events. Deepfakes' continued development may reduce the effectiveness of security systems that use facial recognition technologies for authentication. Furthermore, the real-looking characteristics of deepfakes have allowed for the spread of misinformation, hoaxes, and fraud online. In response, Intel announced "FakeCatcher," a new technology that detects deepfake media with a 96 percent accuracy rate. Deepfakes use Machine Learning (ML) and Artificial Intelligence (AI) technology to create accurate impressions of celebrities and politicians doing and saying things they have not. Existing technologies, which use deep learning to investigate signs of digital manipulation, can take hours to dispel web surfers' trust in a deepfake. According to a press release, Intel's FakeCatcher can detect a deepfake in real-time by analyzing what makes humans, which is blood flow, in video pixels. Intel's technology can detect changes in the color of a person's veins as blood circulates through the body. Blood flow signals are then collected from the face and translated by algorithms to determine whether a video is real or a deepfake. In order to avoid negative consequences, it is becoming increasingly important to have software that can help identify deepfakes. Some deepfake videos and images are graphic in nature, while others foster media distrust. Scammers have previously used deepfakes to pose as job seekers in order to gain access to sensitive company information. They have also been used to make inflammatory statements by impersonating prominent political figures. This article continues to discuss the dangers of deepfakes and the technology developed by Intel to detect deepfakes.