A New and Simpler Deepfake Method That Outperforms Prior Approaches

A collaboration between a Chinese AI research group and US-based researchers has developed what may be the first real innovation in deepfakes technology since the phenomenon emerged four years ago. The new method can perform faceswaps that outperform all other existing frameworks on standard perceptual tests, without needing to exhaustively gather and curate large dedicated datasets and train them for up to a week for just a single identity. For the examples presented in the new paper, models were trained on the entirety of two popular celebrity datasets, on one NVIDIA Tesla P40 GPU for about three days. In this sample from a video in supplementary materials provided by one of the authors of the new paper, Scarlett Johansson's face is transferred onto the source video. CihaNet removes the problem of edge-masking when performing a swap, by forming and enacting deeper relationships between the source and target identities, meaning an end to'obvious borders' and other superimposition glitches that occur in traditional deepfake approaches.