Deepfake detection faces a critical generalization hurdle, with performance
deteriorating when there is a mismatch between the distributions of training
and testing data. A broadly received explanation is the tendency of these
detectors to be overfitted to forgery-specific artifacts, rather than learning
features that are widely applicable across various forgeries. To address this
issue, we propose a simple yet effective detector called LSDA
(\underline{L}atent \underline{S}pace \underline{D}ata
\underline{A}ugmentation), which is based on a heuristic idea: representations
with a wider variety of forgeries should be able to learn a more generalizable
decision boundary, thereby mitigating the overfitting of method-specific
features (see Figure. 1). Following this idea, we propose to enlarge the
forgery space by constructing and simulating variations within and across
forgery features in the latent space. This approach encompasses the acquisition
of enriched, domain-specific features and the facilitation of smoother
transitions between different forgery types, effectively bridging domain gaps.
Our approach culminates in refining a binary classifier that leverages the
distilled knowledge from the enhanced features, striving for a generalizable
deepfake detector. Comprehensive experiments show that our proposed method is
surprisingly effective and transcends state-of-the-art detectors across several
widely used benchmarks.

通过扩充潜在空间中的伪造特征的变化和模拟，在改善深假检测器过拟合的同时，实现了更加泛化的决策边界，从而使得所提出的方法在多个广泛使用的基准测试中超越了现有技术。