A Transformer-Based DeepFake-Detection Method for Facial Organs

Abstract

Nowadays, deepfake detection on subtle-expression manipulation, facial-detail modification, and smeared images has become a research hotspot. Existing deepfake-detection methods on the whole face are coarse-grained, where the details are missing due to the negligible manipulated size of the image. To address the problems, we propose to build a transformer model for a deepfake-detection method by organ, to obtain the deepfake features. We reduce the detection weight of defaced or unclear organs to prioritize the detection of clear and intact organs. Meanwhile, to simulate the real-world environment, we build a Facial Organ Forgery Detection Test Dataset (FOFDTD), which includes the images of mask face, sunglasses face, and undecorated face collected from the network. Experimental results on four benchmarks, i.e., FF++, DFD, DFDC-P, Celeb-DF, and for FOFDTD datasets, demonstrated the effectiveness of our proposed method.