As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods typically rely on data replay or coarse binary supervision, which fails to explicitly constrain the feature space, leading to severe feature drift and catastrophic forgetting. To address this, we propose AIFIND, Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection, which leverages semantic anchors to stabilize incremental learning. We design the Artifact-Driven Semantic Prior Generator to instantiate invariant semantic anchors, establishing a fixed coordinate system from low-level artifact cues. These anchors are injected into the image encoder via Artifact-Probe Attention, which explicitly constrains volatile visual features to align with stable semantic anchors. Adaptive Decision Harmonizer harmonizes the classifiers by preserving angular relationships of semantic anchors, maintaining geometric consistency across tasks. Extensive experiments on multiple incremental protocols validate the superiority of AIFIND.

翻译：随着伪造类型持续涌现，增量人脸伪造检测已成为关键研究范式。然而，现有方法通常依赖数据重放或粗粒度二元监督，无法显式约束特征空间，导致严重的特征漂移和灾难性遗忘。为此，我们提出AIFIND——一种面向增量人脸伪造检测的伪影感知细粒度对齐解释方法，通过语义锚点稳定增量学习。我们设计了伪影驱动语义先验生成器，从底层伪影线索中实例化不变语义锚点，构建固定坐标系。通过伪影探针注意力机制将这些锚点注入图像编码器，显式约束易变视觉特征与稳定语义锚点对齐。自适应决策协调器通过保留语义锚点的角度关系来统一各分类器，保持跨任务的几何一致性。在多种增量协议上的大量实验验证了AIFIND的优越性能。