Robust face forgery detection integrating local texture and global texture information

Facial forgery technology is advancing rapidly, leading to significant social security concerns. In recent years, as forgery technologies and types continue to emerge, many methods struggle to strike a balance between accuracy and robustness. Most existing methods rely on CNN to extract high-quality forged face clues but often overlook inherent forgery traces. Consequently, they may overfit the training dataset and perform poorly on data from diverse sources or be subjected to various post-processing operations. To address this challenge, we propose leveraging multi-scale texture information to expose subtle artifacts in RGB space. To achieve this, we devise a two-stream detection architecture that integrates texture features and RGB features. We analyze forgery traces using global large texture information and local detailed texture information separately. Additionally, we design a feature pyramid to fuse these two texture features and employ an attention mechanism to enhance the features of both streams. By examining forgery traces from multiple perspectives, we have developed an adaptive feature fusion module to facilitate interactive feature fusion between the two streams. We conduct extensive experiments on various benchmark datasets and compare our method with recent state-of-the-art (SOTA) methods to demonstrate its effectiveness. Our code will be provided at https://github.com/hryyyy/MST .