UTokyo’s Novel Self-Blended Images Approach Achieves SOTA Results in Deepfake Detection

A research team from the University of Tokyo addresses the challenge of deepfake detection in their new paper Detecting Deepfakes with Self-Blended Images, proposing self-blended images (SBIs), a novel synthetic training data approach that outperforms state-of-the-art methods on unseen manipulations and scenes for deepfake detection tasks.

ai artificial intelligence deepfake deepfake-detection detection generative adversarial networks images machine learning machine learning & data science ml research sota technology