# ❗ Updated WACV 2026 paper announcement ❗

We are excited to announce that our [new paper](https://arxiv.org/abs/2508.06248) has been accepted to WACV 2026! The updated version includes additional experiments, models, and insights. Check out the latest version on [GitHub](https://github.com/yermandy/GenD).

---

## Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection

[![arXiv Badge](https://img.shields.io/badge/arXiv-B31B1B?logo=arxiv&logoColor=FFF)](https://arxiv.org/abs/2503.19683)
[![Hugging Face Badge](https://img.shields.io/badge/Hugging%20Face-FFD21E?logo=huggingface&logoColor=000)](https://huggingface.co/yermandy/deepfake-detection)

This is the official repository for the paper:

**[Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection](https://arxiv.org/abs/2503.19683)**.

### Abstract

> This paper tackles the challenge of detecting partially manipulated facial deepfakes, which involve subtle alterations to specific facial features while retaining the overall context, posing a greater detection difficulty than fully synthetic faces. We leverage the Contrastive Language-Image Pre-training (CLIP) model, specifically its ViT-L/14 visual encoder, to develop a generalizable detection method that performs robustly across diverse datasets and unknown forgery techniques with minimal modifications to the original model. The proposed approach utilizes parameter-efficient fine-tuning (PEFT) techniques, such as LN-tuning, to adjust a small subset of the model's parameters, preserving CLIP's pre-trained knowledge and reducing overfitting. A tailored preprocessing pipeline optimizes the method for facial images, while regularization strategies, including L2 normalization and metric learning on a hyperspherical manifold, enhance generalization. Trained on the FaceForensics++ dataset and evaluated in a cross-dataset fashion on Celeb-DF-v2, DFDC, FFIW, and others, the proposed method achieves competitive detection accuracy comparable to or outperforming much more complex state-of-the-art techniques. This work highlights the efficacy of CLIP's visual encoder in facial deepfake detection and establishes a simple, powerful baseline for future research, advancing the field of generalizable deepfake detection.


## Set up environment

``` bash
conda create --name dfdet python=3.12 uv
conda activate dfdet
uv pip install -r requirements.txt
```

## Minimal inference example

**❗ Important note**: sample images are already preprocessed. To get the same results as in the paper, you need to preprocess images using DeepfakeBench [preprocessing](https://github.com/SCLBD/DeepfakeBench/blob/fb6171a8e1db2ae0f017d9f3a12be31fd9e0a3fb/preprocessing/preprocess.py) pipeline.

### Minimal dependencies (torch + transformers)

This example requires only `torch` and `transformers` to run. This is an easy-to-integrate solution. The model has been traced and saved to a [`model.torchscript`](https://huggingface.co/yermandy/deepfake-detection/tree/main) file. Run:

``` bash
python inference_torchscript.py
```

Results might be a little bit different than in **precise inference** ↓

### Precise inference (full dependencies)

Read `inference.py`, it automatically downloads the model from [huggingface](https://huggingface.co/yermandy/deepfake-detection/tree/main) and runs inference on sample images.

``` bash
python inference.py
```

## Training

### Minimal example without external data

#### Run Training

You can adjust training configuration in `get_train_config` function in `run.py` or override them with command line arguments. Command line arguments have higher priority.

Example changing configurations in `get_train_config`:

1. Set `config.wandb = True` for logging to wandb

2. Set `config.devices = [2]` for using GPU number 2

``` bash
python run.py --train
```

#### Run testing (for example, on other dataset)

``` bash
python run.py --test
```

---

### Full training

#### Prepare the dataset

To fully train the model, you need to download datasets, preprocess them, and create a file with paths to the images.

For example, if you want to work with the [FaceForensics++](https://github.com/ondyari/FaceForensics) dataset, follow these steps:

1. Download the dataset first from the [official source](https://github.com/ondyari/FaceForensics)

2. Preprocess the dataset using [DeepfakeBench](https://github.com/SCLBD/DeepfakeBench)

3. Place images in the recommended directory structure: `datasets / <dataset_name> / <source_name> / <video_name> / <frame_name>`, see `src/dataset/deepfake.py` for more details

``` bash
datasets
└── FF
    ├── DF
    │   └── 000_003
    │       ├── 025.png
    │       └── 038.png
    ├── F2F
    │   └── 000_003
    │       ├── 019.png
    │       └── 029.png
    ├── FS
    │   └── 000_003
    │       ├── 019.png
    │       └── 029.png
    ├── NT
    │   └── 000_003
    │       ├── 019.png
    │       └── 029.png
    └── real
        └── 000
            ├── 025.png
            └── 038.png
```

4. Create files with paths to images similar to the ones in `config/datasets` directory. Get inspired by this script:

``` bash
sh scripts/prepare_FF.sh
```

#### Run training

Adjust training configuration as needed before executing the command below:

``` bash
python run.py --train
```

### Cite

``` bibtex
@article{yermakov-2025-deepfake-detection,
    title={Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection}, 
    author={Andrii Yermakov and Jan Cech and Jiri Matas},
    year={2025},
    eprint={2503.19683},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2503.19683}, 
}
```