Deepfakes have become rather common particularly in the last couple of years where the technology has become better and easier to use. This has been a boon for propagandists who like to feed conspiracy theorists with seemingly plausible evidence of rival's wrong doings.
Intel claims the product has a 96% accuracy rate and works by analyzing the subtle "blood flow" in video pixels to return results in milliseconds. Ilke Demir, senior staff research scientist in Intel Labs, designed FakeCatcher in collaboration with Umur Ciftci from the State University of New York at Binghamton. The product uses Intel hardware and software, runs on a server and interfaces through a web-based platform.
Unlike most deep learning-based deepfake detectors, which look at raw data to pinpoint inauthenticity, FakeCatcher is focused on clues within the videos. It is based on photoplethysmography, or PPG, a method for measuring the amount of light that is absorbed or reflected by blood vessels in living tissue.
When the heart pumps blood, it goes to the veins, which change color. With FakeCatcher, PPG signals are collected from 32 locations on the face, she explained, and then PPG maps are created from the temporal and spectral components.
The software uses those maps and train a convolutional neural network on top of the PPG maps to classify them as fake and real.
Chipzilla is working on developing something it calls Trusted Media to develop a solution to deepfakes.
"The golden point of what we envision is having an ensemble of all of these AI models, so we can provide an algorithmic consensus about what is fake and what is real," a spokesIntel said.