通过稳定特征空间防止遗忘，提升人脸伪造检测的持续学习能力
AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection
- 用伪造痕迹生成语义锚点，建立稳定的特征坐标系
- 通过注意力机制让图像特征对齐固定锚点，抑制特征漂移
- 适合需要长期更新的伪造检测系统，尤其在数据增量场景
随着伪造技术持续涌现，增量式人脸伪造检测（IFFD）已成为关键范式。然而，现有方法多依赖数据重放或粗粒度二分类监督，无法显式约束特征空间，导致严重特征漂移和灾难性遗忘。为此，我们提出AIFIND：一种基于伪造痕迹感知的细粒度对齐增量检测方法。通过设计伪影驱动的语义先验生成器，从低层伪影线索中实例化不变的语义锚点，构建固定的特征坐标系。这些锚点通过伪影探针注意力注入图像编码器，显式约束易变的视觉特征与稳定锚点对齐。自适应决策调和器通过保留语义锚点间的角度关系，维持跨任务的几何一致性。在多个增量协议上的大量实验验证了AIFIND的优越性。
原文摘要 · Abstract (English)
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.
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