<p>The escalating sophistication of deepfake technologies poses a formidable challenge to the reliability of digital identity verification, necessitating robust face forgery detection systems. Existing methods often struggle with generalization across unseen scenarios, primarily due to their reliance on handcrafted features and a lack of robustness against domain-specific variations. Moreover, the sensitivity of these methods to minor perturbations in forgery methods limits their applicability in real-world settings. To overcome these issues, we introduce a novel framework to boost the generalizability of face forgery detection via discriminative feature analysis and normalization. Specifically, we design a Progressive Texture Mining (PTM) module that leverages center difference convolution and a Global Context module to extract both local fine-grained artifacts and global texture information during the early learning stages. Then, Forgery Aware Normalization (FAN) aligns feature distributions across different forgery styles and classes, promoting intra-class compactness and inter-class separability. Furthermore, Discriminative Feature Disentangle (DFD) addresses the challenge of domain-specific feature sensitivity by selectively disentangling and discarding sensitive channels. Through rigorous evaluation on five public datasets, our method demonstrates superior generalization performance, achieving high accuracy across a spectrum of forgery techniques compared with other state-of-the-art methods.</p>