Texture and artifact decomposition for improving generalization in deep-learning-based deepfake detection

The harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model's generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities.