Environment-Invariant Subspace Learning for Generalizable Deepfake Detection

2026-08-18

AI Summary

为解决深伪检测中的环境干扰问题，提出环境不变子空间学习框架EISL，通过可学习低秩投影分解特征，增强模型对未知伪造类型和环境变化的鲁棒性。

Abstract

Cross-distribution generalization remains a critical bottleneck in deepfake detection. While recent efforts leverage the semantic priors of large-scale visual foundation models (VFMs), a noteworthy yet underexplored challenge remains: the susceptibility of these semantic priors to environmental interference from factors such as lighting and style. Crucially, this interference establishes spurious correlations between forgery cues and environmental patterns that severely limit generalization. To address this fundamental challenge, we propose an innovative Environment-Invariant Subspace Learning (EISL) framework. The core contribution of EISL is that it aims to disentangle features into orthogonal forgery-relevant invariant factors and environment-related residual factors via a learnable low-rank projection. To facilitate robust feature disentanglement, we also design an Environmental Intervention module that generates diverse and challenging intervention pairs, simulating out-of-distribution environmental shifts to guide the model toward discovering truly invariant forgery representations. Experiments across cross-dataset, cross-generator, whole-face synthesis, and corruption settings show consistent gains and competitive or leading performance against strong detectors, demonstrating improved robustness to unseen forgery types and environmental variations. This work provides a new perspective and a valuable exploration for understanding and tackling the generalization barriers of VFMs in deepfake detection.

Problem

• cross-distribution generalization
• deepfake detection
• environmental interference
• spurious correlations
• semantic priors

Innovation

• Environment-Invariant Subspace Learning
• low-rank projection
• Environmental Intervention module
• generalization