Researchers have developed a deepfake detection method that specifically focuses on a lightweight, low training complexity, and high-performance face biometrics technique with ideal size, weight, and power (SWaP).
Deepfake refers to artificial intelligence-synthesized, hyper-realistic video content that falsely depicts individuals saying or doing something. The researchers set out to tackle the significant threat that deepfake poses to our society and national security. The result is an innovative technological solution called DefakeHop for deepfake detection.
Combining machine learning, signal analysis, and computer vision techniques, the researchers developed an innovative theory and mathematical framework, the Successive Subspace Learning, or SSL, as innovative neural network architecture. SSL is an entirely new mathematical framework for neural network architecture developed from signal transform theory. It offers a new signal representation and process that involves multiple transform matrices in cascade. It is very suitable for high-dimensional data that have short-, mid-, and long-range covariance structures. It is a complete data-driven unsupervised framework, offers a brand new tool for image processing and understanding tasks such as face biometrics.