# Generalized Zero and Few-Shot Transfer for Facial Forgery Detection

arXiv: https://arxiv.org/abs/2006.11863 · HTML: https://mlsanity.com/paper/2006.11863/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [General Facial Representation Learning in a Visual-Linguistic Manner](https://mlsanity.com/paper/2112.03109/) (2021)
- [MARLIN: Masked Autoencoder for facial video Representation LearnINg](https://mlsanity.com/paper/2211.06627/) (2022)
- [SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes](https://mlsanity.com/paper/2211.11296/) (2022)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (2021)
- [TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations](https://mlsanity.com/paper/2112.09151/) (2021)
- [Learning Pairwise Interaction for Generalizable DeepFake Detection](https://mlsanity.com/paper/2302.13288/) (2023)
- [DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation](https://mlsanity.com/paper/2312.10680/) (2023)
- [Diffusion Deepfake](https://mlsanity.com/paper/2404.01579/) (2024)
- [A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features](https://mlsanity.com/paper/2208.00788/) (2022)
- [Aggregating Layers for Deepfake Detection](https://mlsanity.com/paper/2210.05478/) (2022)
- [Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes](https://mlsanity.com/paper/2311.17655/) (2023)
- [Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation](https://mlsanity.com/paper/2403.08783/) (2024)
- [Rethinking the Use of Vision Transformers for AI-Generated Image Detection](https://mlsanity.com/paper/2512.04969/) (2025)
- [Self-supervised GAN Detector](https://mlsanity.com/paper/2111.06575/) (2021)
- [Beyond Deepfake vs Real: Facial Deepfake Detection in the Open-Set Paradigm](https://mlsanity.com/paper/2503.08055/) (2025)
- [A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation](https://mlsanity.com/paper/2504.08411/) (2025)
- [Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation](https://mlsanity.com/paper/2505.12339/) (2025)
- [Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection](https://mlsanity.com/paper/2506.02857/) (2025)

## Abstract (arXiv)

We propose Deep Distribution Transfer(DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery detection. We examine how well a model (pre-)trained with one forgery creation method generalizes towards a previously unseen manipulation technique or different dataset. To facilitate this transfer, we introduce a new mixture model-based loss formulation that learns a multi-modal distribution, with modes corresponding to class categories of the underlying data of the source forgery method. Our core idea is to first pre-train an encoder neural network, which maps each mode of this distribution to the respective class labels, i.e., real or fake images in the source domain by minimizing wasserstein distance between them. In order to transfer this model to a new domain, we associate a few target samples with one of the previously trained modes. In addition, we propose a spatial mixup augmentation strategy that further helps generalization across domains. We find this learning strategy to be surprisingly effective at domain transfer compared to a traditional classification or even state-of-the-art domain adaptation/few-shot learning methods. For instance, compared to the best baseline, our method improves the classification accuracy by 4.88% for zero-shot and by 8.38% for the few-shot case transferred from the FaceForensics++ to Dessa dataset.