Deepfake detection still faces significant challenges in cross-dataset and real-world complex scenarios. The root cause lies in the high diversity of artifact distributions introduced by different forgery methods, while pretrained models tend to disrupt their original general semantic structures when adapting to new artifacts. Existing approaches usually rely on indiscriminate global parameter updates or introduce additional supervision signals, making it difficult to effectively model diverse forgery artifacts while preserving semantic stability. To address these issues, this paper proposes a deepfake detection method based on Multi-Artifact Subspaces and selective layer masks (MASM), which explicitly decouples semantic representations from artifact representations and constrains the fitting strength of artifact subspaces, thereby improving generalization robustness in cross-dataset scenarios. Specifically, MASM applies singular value decomposition to model weights, partitioning pretrained weights into a stable semantic principal subspace and multiple learnable artifact subspaces. This design enables decoupled modeling of different forgery artifact patterns while preserving the general semantic subspace. On this basis, a selective layer mask strategy is introduced to adaptively regulate the update behavior of corresponding network layers according to the learning state of each artifact subspace, suppressing overfitting to any single forgery characteristic. Furthermore, orthogonality constraints and spectral consistency constraints are imposed to jointly regularize multiple artifact subspaces, guiding them to learn complementary and diverse artifact representations while maintaining a stable overall spectral structure.

翻译：深度伪造检测在跨数据集和真实世界复杂场景中仍面临重大挑战。其根本原因在于不同伪造方法引入的伪影分布具有高度多样性，而预训练模型在适应新伪影时往往会破坏其原有的通用语义结构。现有方法通常依赖无差别的全局参数更新或引入额外的监督信号，难以在保持语义稳定性的同时有效建模多样化的伪造伪影。为解决这些问题，本文提出一种基于多伪影子空间与选择性层掩码（MASM）的深度伪造检测方法，该方法显式解耦语义表示与伪影表示，并约束伪影子空间的拟合强度，从而提升跨数据集场景下的泛化鲁棒性。具体而言，MASM对模型权重应用奇异值分解，将预训练权重划分为稳定的语义主控子空间与多个可学习的伪影子空间。该设计能够在保持通用语义子空间的同时，对不同伪造伪影模式进行解耦建模。在此基础上，引入选择性层掩码策略，根据各伪影子空间的学习状态自适应调节对应网络层的更新行为，抑制对任何单一伪造特征的过拟合。此外，通过施加正交性约束与谱一致性约束共同正则化多个伪影子空间，引导其学习互补且多样化的伪影表示，同时维持稳定的整体谱结构。