Identity-Referenced Deepfake Detection with Contrastive Learning

Abstract

With current advancements in deep learning technology, it is becoming easier to create high-quality face forgery videos, causing concerns about the misuse of deepfake technology. In recent years, research on deepfake detection has become a popular topic. Many detection methods have been proposed, most of which focus on exploiting image artifacts or frequency domain features for detection. In this work, we propose using real images of the same identity as a reference to improve detection performance. Specifically, a real image of the same identity is used as a reference image and input into the model together with the image to be tested to learn the distinguishable identity representation, which is achieved by contrastive learning. Our method achieves superior performance on both FaceForensics++ and Celeb-DF with relatively little training data, and also achieves very competitive results on cross-manipulation and cross-dataset evaluations, demonstrating the effectiveness of our solution.