GM-DF: Generalized Multi-Scenario Deepfake Detection
Yingxin Lai, Hongyang Wang, Jing Yang, Xiangui Kang, Bin Li, Linlin Shen, Zitong Yu
Abstract
Recent advances in face forgery detection have shown strong in-domain performance but often fail to generalize to out-of-distribution data, especially when confronted with unseen manipulation techniques or domain shifts (e.g., lighting conditions, camera noise). We propose a novel Mixture-of-Experts framework, termed GM-DF, that decouples domain-specific and domain-invariant features to tackle cross-domain face forgery detection. Our method builds upon a foundation model (CLIP) and incorporates three key modules: (1) Dataset-Embedding Generator that leverages lightweight expert layers and database-aware feature normalization to adaptively modulate features at a per-domain level, capturing idiosyncratic cues without overfitting; (2) Multi-Dataset Representation mechanism that fuses these expert embeddings using scaled dot-product attention and integrates a mask image modeling (MIM) task to amplify local forgery artifacts; (3) Meta-Domain-Embedding Optimizer, inspired by MAML, which alternates between domain-specific (inner-loop) and domain-invariant (outer-loop) updates to facilitate rapid adaptation on new domains. Additionally, inspired by [13] (Yossi Gandelsman, Alexei A Efros, and Jacob Steinhardt. 2024. Interpreting the second-order effects of neurons in clip. arXiv preprint arXiv:2406.04341 (2024)) we introduce second-order feature propagation in the intermediate layers of CLIP to enhance fine-grained artifact cues and propose domain-class disentangled prompts to flexibly encode multi-domain text representations. Together, these strategies enable GM-DF to learn robust, shared forgery cues while preserving essential domain nuances. Our extensive experiments on multiple cross-domain benchmarks demonstrate that GM-DF significantly outperforms state-of-the-art approaches in both detection accuracy and domain transferability, reducing reliance on superficial artifacts and improving generalization to unseen forgeries. Importantly, our design requires minimal overhead beyond standard CLIP, making GM-DF both effective and computationally efficient for real-world face forgery detection.