Within 3DMM Space: Exploring Inherent 3D Artifact for Video Forgery Detection
Chunlei Peng, Tianmin Xu, Decheng Liu, Nannan Wang, Xinbo Gao · IEEE Transactions on Information Forensics and Security · 2025
Recently, the breathtaking development and potential misuse of deepfake technology has raised numerous privacy and security concerns, triggering widespread apprehension. Existing deepfake detection methods focus on the analysis of local regions for faces, such as mouth movement, eye blinking frequency, etc., which, however, are limited in their ability to capture the global inconsistencies present in forged faces. Some researchers attempt to seize 3D artifacts related to facial global information, but typically treat the 3D information as mere input, lacking the in-depth analysis. To address these shortcomings and mine the inherent and delicate 3D artifacts in the forged faces, this paper innovatively proposes the 3D Artifact Detector (3DAD) method, which leverages the spatio-temporal inconsistency on the 3D semantic space in the forgery videos to uncover the deepfake clues. Specifically, we employ 3D Analysis Unit (3DAU) to pre-train the face reconstruction task within 3D Morphable Model (3DMM) space, thereby obtaining the high-level inherent 3d representation. Concurrently, for the multi-levels of information in the face, we utilize the Texture Perception Unit (TPU) to extract the texture information in the low-level semantic space of the images. Ultimately we feed the two distinct modalities into the spatiotemporal fusion model for final detection. Through extensive intra- and cross-dataset experiments on publicly available datasets, we demonstrate the effectiveness and generalizability of the proposed method. The source code is available at https://github.com/Cookie-XT/3DAD.