CoDE (Contrastive Deepfake Embeddings)

Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities

(ECCV 2024)

Table of Contents

1. Training Dataset
2. Citation

Training Dataset

🎯 Project web page | Paper | Dataset web page | D³ Test Set | 🤗 HuggingFace Dataset | 🤗 HuggingFace Model |

The Diffusion-generated Deepfake Detection (D³) Dataset is a comprehensive collection designed for large-scale deepfake detection. It includes 9.2 million generated images, created using four state-of-the-art diffusion model generators. Each image is generated based on realistic textual descriptions from the LAION-400M dataset.

• Images: 11.5 million images
• Records: 2.3 million records
• Generators: Stable Diffusion 1.4, Stable Diffusion 2.1, Stable Diffusion XL, and DeepFloyd IF
• Aspect Ratios: 256x256, 512x512, 640x480, 640x360
• Encodings: BMP, GIF, JPEG, TIFF, PNG

The D³ dataset is part of the European Lighthouse on Secure and Safe AI (ELSA) project, which aims to develop effective solutions for detecting and mitigating the spread of deepfake images in multimedia content.

To try D³ you can access it using

from datasets import load_dataset
elsa_data = load_dataset("elsaEU/ELSA_D3", split="train", streaming=True)

The test set of D³ is available at this link D³ Test Set

Inference

Install the requirements by

pip install requirements.txt

After downloading the test set of D³, you can use the following code to load the dataset and run the inference on the CoDE model.

Substitute the path of the directories in dataset_path_d3.py

cd CoDE_model
python validate_d3.py --classificator_type "linear"
# options for classificator_type are ["linear", "knn", "svm"]

Citation

Please cite with the following BibTeX:

@inproceedings{baraldi2024contrastive,
  title={{Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities}},
  author={Baraldi, Lorenzo and Cocchi, Federico and Cornia, Marcella and Baraldi, Lorenzo and Nicolosi, Alessandro and Cucchiara, Rita},
  booktitle={Proceedings of the European Conference on Computer Vision},
  year={2024}
}