Duplicate from ComplexDataLab/OpenFake

Co-authored-by: Victor Livernoche <vicliv@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: prompt
+    dtype: string
+  - name: label
+    dtype: string
+  - name: model
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1051540257907.984
+    num_examples: 1870684
+  - name: test
+    num_bytes: 33418712589.0
+    num_examples: 59658
+  download_size: 1083933904266
+  dataset_size: 1084958970496.984
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+license: cc-by-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+size_categories:
+- 100K<n<1M
+---
+
+
+# Dataset Card for OpenFake
+
+## Dataset Details
+
+### Dataset Description
+
+OpenFake is a dataset designed for evaluating deepfake detection and misinformation mitigation in the context of politically relevant media. It includes high-resolution real and synthetic images generated from prompts with political relevance, including faces of public figures, events (e.g., disasters, protests), and multimodal meme-style images with text overlays. Each image includes structured metadata with its prompt, source model (for synthetic), and human-annotated or pipeline-assigned labels.
+
+* **Authors:** Victor Livernoche; Akshatha Arodi; Andreea Musulan; Zachary Yang; Adam Salvail; Gaétan Marceau Caron; Jean-François Godbout; Reihaneh Rabbany  
+* **Curated by:** Victor Livernoche; Akshatha Arodi; Jie Zang  
+* **Funded by:** CIFAR AI Chairs Program; Centre for the Study of Democratic Citizenship (CSDC); IVADO; Canada First Research Excellence Fund; Mila (financial support and computational resources)  
+* **Language(s) (prompts):** English  
+* **License:** CC-BY-SA-4.0. Note: subsets produced with proprietary generators are released under non-commercial terms due to “non-compete” clauses; see paper for details.
+
+### Dataset Sources
+
+- **Repository:** [https://huggingface.co/datasets/ComplexDataLab/OpenFake](https://huggingface.co/datasets/ComplexDataLab/OpenFake)  
+- **Arena (crowdsourced adversarial platform):** [https://huggingface.co/spaces/CDL-AMLRT/OpenFakeArena](https://huggingface.co/spaces/CDL-AMLRT/OpenFakeArena)
+
+## Uses
+
+### Direct Use
+
+* Benchmarking binary classifiers for real vs. synthetic image detection  
+* Evaluating robustness across models and content types (faces, events, memes)  
+* Training adversarially robust detectors via community submissions (OpenFake Arena)  
+
+### Out-of-Scope Use
+
+* Training generative models directly on the dataset without consent  
+* Any use of personal imagery that violates platform rules or privacy  
+
+## Dataset Structure
+
+* `image`: image (real or synthetic)  
+* `label`: `real` or `fake`  
+* `model`: the model that generated the synthetic image  
+* `prompt`: prompt used to generate the synthetic image or caption for a real image  
+
+Train/test split is balanced by label and curated for visual and topical diversity. No image overlaps between splits.
+
+**Unused metadata:** `unused_metadata.csv` contains URLs and prompts for images not included in the train/test splits.
+
+## Models Covered
+
+Synthetic images were generated from a diverse set of state-of-the-art generators, including:
+
+- Stable Diffusion **1.5**, **2.1**, **XL**, **3.5**  
+- Flux **1.0-dev**, **1.1-Pro**, **1.0-Schnell**  
+- Midjourney **v6**, **v7**  
+- **DALL·E 3**, **Imagen 3**, **Imagen 4**  
+- **GPT Image 1**, **Ideogram 3.0**, **Grok-2**, **HiDream-I1**, **Recraft v3**, **Chroma**  
+- Plus 10 community LoRA/finetuned variants of SD 1.5/XL and Flux-dev  
+
+All images are produced at ~1 MP with varied aspect ratios reflecting common social-media formats.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The goal is to fill a gap in deepfake detection datasets by covering high-quality, politically sensitive synthetic imagery and going beyond face-only benchmarks to include events and hybrid image-text memes. The dataset pairs ~3M politically themed real images (filtered from LAION-400M using Qwen2.5-VL) with ~963k synthetic counterparts, and is complemented by the OpenFake Arena for continual hard negative generation.
+
+### Source Data
+
+**Real images.** Selected from LAION-400M and filtered with Qwen2.5-VL to retain faces and politically salient or newsworthy events. Detailed captions are produced to drive T2I generation and Arena prompts.  
+
+**Synthetic images.** Generated using the model list above from a shared prompt bank. Open-source models follow documented generation settings for reproducibility.  
+
+#### Who are the source data producers?
+
+* Real: news outlets, political users, and public social-media posts  
+* Synthetic: produced by researchers and community contributors from prompts; Arena submissions are gated by CLIP for prompt relevance and logged with metadata  
+
+#### Personal and Sensitive Information
+
+Source data was filtered to reduce personal or sensitive content; see the paper’s ethics and licensing notes.
+
+## Bias, Risks, and Limitations
+
+There may be overrepresentation of Western political events due to source distribution. Synthetic examples inherit generator biases. Not all labels are exhaustively human-verified. Adversarial use is a risk, mitigated by licensing and the dataset’s focus on detection.
+
+### Recommendations
+
+Use caution when interpreting political narratives in images. Do not use for content generation or facial identity research without additional review.
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@misc{livernoche2025openfakeopendatasetplatform,
+      title={OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection}, 
+      author={Victor Livernoche and Akshatha Arodi and Andreea Musulan and Zachary Yang and Adam Salvail and Gaétan Marceau Caron and Jean-François Godbout and Reihaneh Rabbany},
+      year={2025},
+      eprint={2509.09495},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2509.09495}, 
+}
+```
+
+
+**APA:**
+
+Livernoche, V., Arodi, A., Musulan, A., Yang, Z., Salvail, A., Marceau Caron, G., Godbout, J.-F., & Rabbany, R. (2025). OpenFake: An open dataset and platform toward large-scale deepfake detection. arXiv. https://arxiv.org/abs/2509.09495
+
+## More Information
+
+For questions, errors, or contributions, visit the GitHub or HF repository.
+
+## Dataset Card Authors
+
+Victor Livernoche
+
+## Dataset Card Contact
+
+victor.livernoche@mail.mcgill.ca

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