Deepfake face discrimination based on self-attention mechanism

With the rapid progress of deepfake technology, the improper use of manipulated images and videos presenting synthetic faces has arisen as a noteworthy concern, thereby posing threats to both daily life and national security. While numerous CNN based deepfake face detection methods were proposed, most of the existing approaches encounter challenges in effectively capturing the image contents across different scales and positions. In this paper, we present a novel two-branch structural network, referred to as the Self-Attention Deepfake Face Discrimination Network (SADFFD). Specifically, a branch incorporating cascaded multi self-attention mechanism (SAM) modules, is parallelly integrated with EfficientNet-B4 (EffB4). The multi SAM branch supplies additional features that concentrate on image regions essential for discriminating between real and fake. The EffB4 network is adopted because of its efficiency by adjusting the resolution, depth, and width of the network. According to our comprehensive experiments conducted on FaceForensics++, Celeb-DF, and our self-constructed SAMGAN3 datasets, the proposed SADFFD demonstrated the highest detection accuracy, averaging 99.01% in FaceForensics++, 98.65% in Celeb-DF, and an impressive 99.99% in SAMGAN3, surpassing other state-of-the-art (SOTA) methods.