Author

Hao Jia

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Preprint Aug 2026

Environment-Invariant Subspace Learning for Generalizable Deepfake Detection

This work proposes an innovative Environment-Invariant Subspace Learning (EISL) framework, which aims to disentangle features into orthogonal forgery-relevant invariant factors and environment-related residual factors via a learnable low-rank projection and designs an Environmental Intervention module that generates diverse and challenging intervention pairs.

Shenghao Chen, Hao Jia, Chen Li et al. · 0 citations