Facial expression-based detection method for deepfake by generative artificial intelligence (ai)

Abstract

A facial expression-based detection method for deepfake by generative artificial intelligence (AI) constructs an AIR-Face facial dataset for generative AI-created face detection training, and uses an untrained information feature space for real and fake classification. Nearest linear detection is performed in this space to significantly improve the generalization ability of detecting fake images, especially those created by new methods such as diffusion models or autoregressive models. The detection method improves the performance of extracting features of generative AI-created faces through phased trainings, and detects generative AI-created faces through the feature space. Compared with other methods, the detection method scientifically and effectively improves the accuracy of generative AI-created face recognition, and fully mines the potential semantic information of generative AI-created faces through phased trainings. In this way, the detection method improves reliability and accuracy in generative AI-created face detection, meeting the needs of generative AI-created face detection.