Deepfake detection remains highly challenging, particularly in cross-dataset scenarios and complex real-world settings. This challenge mainly arises because artifact patterns vary substantially across different forgery methods, whereas adapting pretrained models to such artifacts often overemphasizes forgery-specific cues and disturbs semantic representations, thereby weakening generalization. Existing approaches typically rely on full-parameter fine-tuning or auxiliary supervision to improve discrimination. However, they often struggle to model diverse forgery artifacts without compromising pretrained representations. To address these limitations, we propose FMSD, a deepfake detection framework built upon Forgery-aware Layer Masking and Multi-Artifact Subspace Decomposition. Specifically, Forgery-aware Layer Masking evaluates the bias-variance characteristics of layer-wise gradients to identify forgery-sensitive layers, thereby selectively updating them while reducing unnecessary disturbance to pretrained representations. Building upon this, Multi-Artifact Subspace Decomposition further decomposes the selected layer weights via Singular Value Decomposition (SVD) into a semantic subspace and multiple learnable artifact subspaces. These subspaces are optimized to capture heterogeneous and complementary forgery artifacts, enabling effective modeling of diverse forgery patterns while preserving pretrained semantic representations. Furthermore, orthogonality and spectral consistency constraints are imposed to regularize the artifact subspaces, reducing redundancy across them while preserving the overall spectral structure of pretrained weights.

翻译：Deepfake检测依然面临巨大挑战，尤其是在跨数据集场景和复杂现实环境中。该挑战主要源于不同伪造方法产生的伪影模式差异显著，而将预训练模型适配于此类伪影时，往往会过度强调伪造特定线索并扰动语义表征，从而削弱泛化能力。现有方法通常依赖全参数微调或辅助监督来提升判别能力，但它们在建模多样化伪造伪影的同时往往难以保持预训练表征的完整性。针对上述局限，我们提出FMSD框架——一种基于伪造感知层掩蔽与多伪影子空间分解的Deepfake检测方法。具体而言，伪造感知层掩蔽通过评估逐层梯度的偏差-方差特性来识别伪造敏感层，从而选择性更新这些层并减少对预训练表征的非必要扰动。在此基础上，多伪影子空间分解进一步利用奇异值分解（SVD）将选定层权重解耦为语义子空间与多个可学习伪影子空间。这些子空间被优化以捕获异质且互补的伪造伪影，在保持预训练语义表征的同时有效建模多样化伪造模式。此外，我们引入正交性与频谱一致性约束来正则化伪影子空间，在降低子空间冗余的同时维持预训练权重的整体频谱结构。