Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a novel Critical Forgery Mining (CFM) framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance. Specifically, we first build a fine-grained triplet and suppress specific forgery traces through prior knowledge-agnostic data augmentation. Subsequently, we propose a fine-grained relation learning prototype to mine critical information in forgeries through instance and local similarity-aware losses. Moreover, we design a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner. The proposed method achieves state-of-the-art forgery detection performance under various challenging evaluation settings.

翻译：人脸伪造检测对于抵御恶意数字面部攻击至关重要。先前的方法主要依赖先验专家知识来捕捉特定伪造线索，例如噪声模式、混合边界和频率伪影。然而，这些方法往往陷入局部最优，导致其鲁棒性和泛化能力有限。为解决这些问题，我们提出了一种新颖的关键伪造挖掘（Critical Forgery Mining，CFM）框架，该框架可灵活地与各种主干网络结合，以提升其泛化与鲁棒性能。具体而言，我们首先构建细粒度三元组，并通过先验知识不可知的数据增强抑制特定伪造痕迹。随后，我们提出一种细粒度关系学习原型，通过实例和局部相似性损失来挖掘伪造中的关键信息。此外，我们设计了一种新颖的渐进式学习控制器，引导模型关注主要特征成分，使其能够以由粗到细的方式学习关键伪造特征。所提方法在多种具有挑战性的评估设置下实现了最先进的伪造检测性能。