Space–Frequency and Global–Local Attentive Networks for Sequential Deepfake Detection

The widespread misinformation generated by deepfake systems has emerged as a significant challenge in the dynamic realm of digital media. It poses threats to credibility, privacy, and security of information in daily life. Moreover, the increasing accessibility to facial editing tools further enables users to alter facial characteristics subtly through a series of intricate steps. To address the issue, we introduce a space–frequency and global–local attentive network (SFGLA-Net) for sequential deepfake detection. This method is designed to identify and analyze the sophisticated manipulated attributes of deepfake images. Specifically, we introduce a space–frequency fusion module to leverage the deep feature extracted in spatial and frequency domains, so as to exploit subtle inconsistencies and artifacts that are not perceptible in the spatial domain alone. Additionally, we design a global–local attention module to pinpoint the manipulated areas more accurately. Extensive experiments demonstrate the superior performance of the proposed method by significantly outperforming existing techniques in sequential deepfake detection. The code is available at https://github.com/guishengzhanga/SFGLA.