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--- |
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license: other |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- image-classification |
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- deepfake-detection |
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- vit |
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- verichain |
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pipeline_tag: image-classification |
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--- |
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# VeriChain Deepfake Detection Model - ViT |
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This repository contains the artifacts for the Vision Transformer (ViT) model fine-tuned for the task of Deepfake detection, developed as part of the VeriChain project. |
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The model is trained to classify an image into one of three categories: **Real**, **AI-Generated**, or **Deepfake**. |
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## Repository Structure |
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The model artifacts in this repository are organized as follows: |
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- **/models/vit-deepfake-model/**: Contains the final, fine-tuned PyTorch model files, ready to be loaded with the `transformers` library. |
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- **/models/onnx/**: Contains the model converted to the ONNX format, optimized for production deployment and inference. |
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- **/assets/**: Contains visual assets for documentation, such as the confusion matrix. |
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## How to Use (PyTorch Model) |
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You can use the fine-tuned PyTorch model directly with the `pipeline` function from the `transformers` library. Make sure to specify the correct `subfolder`. |
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```python |
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from transformers import pipeline |
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from PIL import Image |
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# Load the image classification pipeline with your model |
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# The 'subfolder' parameter points to the directory containing the model files |
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classifier = pipeline( |
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"image-classification", |
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model="einrafh/verichain-deepfake-models", |
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subfolder="models/vit-deepfake-model" |
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) |
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# Load an image you want to classify |
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# Make sure to replace 'path/to/your/image.jpg' with an actual image file |
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try: |
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image = Image.open('path/to/your/image.jpg') |
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results = classifier(image) |
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print(results) |
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except FileNotFoundError: |
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print("Please provide a valid path to an image file.") |
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# Example Output: |
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# [{'label': 'Deepfake', 'score': 0.9985}, {'label': 'AI Generated', 'score': 0.0010}, {'label': 'Real', 'score': 0.0005}] |
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``` |
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## Evaluation Results |
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The model was evaluated on a held-out test set of 2,000 images, achieving near-perfect performance. |
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| Metric | Score | |
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|----------------------|---------| |
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| **Test Accuracy** | **0.9990** | |
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| **F1-Score (Macro)** | **0.9990** | |
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| Test Loss | 0.0202 | |
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### Classification Report |
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| Class | Precision | Recall | F1-Score | |
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|----------------|-----------|--------|----------| |
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| AI Generated | 1.0000 | 0.9970 | 0.9985 | |
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| Deepfake | 0.9970 | 1.0000 | 0.9985 | |
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| Real | 1.0000 | 1.0000 | 1.0000 | |
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### Confusion Matrix |
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The confusion matrix below shows the model's high precision and recall across all classes, with very few misclassifications. |
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## Citation |
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If you use this model in your work, please consider citing this repository. |
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```bibtex |
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@misc{verichain_model_2025, |
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author = {Muhammad Rafly Ash Shiddiqi}, |
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title = {VeriChain Deepfake Detection Model - ViT}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face repository}, |
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howpublished = {\url{[https://huggingface.co/einrafh/verichain-deepfake-models](https://huggingface.co/einrafh/verichain-deepfake-models)}}, |
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} |
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``` |
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## License |
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Copyright (c) 2025 Muhammad Rafly Ash Shiddiqi. |
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