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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/OpenDeepfake-Preview |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-512 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- deepfake |
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- detection |
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- SigLIP2 |
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- art |
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- synthetic |
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--- |
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# open-deepfake-detection |
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> open-deepfake-detection is a vision-language encoder model fine-tuned from `siglip2-base-patch16-512` for binary image classification. It is trained to detect whether an image is fake or real using the *OpenDeepfake-Preview* dataset. The model uses the `SiglipForImageClassification` architecture. |
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> \[!note] |
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> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* |
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> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786) |
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> \[!important] |
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Experimental Model |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Fake 0.9718 0.9155 0.9428 10000 |
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Real 0.9201 0.9734 0.9460 9999 |
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accuracy 0.9444 19999 |
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macro avg 0.9459 0.9444 0.9444 19999 |
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weighted avg 0.9459 0.9444 0.9444 19999 |
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``` |
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--- |
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## Label Space: 2 Classes |
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The model classifies an image as either: |
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``` |
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Class 0: Fake |
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Class 1: Real |
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``` |
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--- |
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## Install Dependencies |
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```bash |
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pip install -q transformers torch pillow gradio hf_xet |
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``` |
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--- |
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## Inference Code |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/open-deepfake-detection" # Updated model name |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Updated label mapping |
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id2label = { |
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"0": "Fake", |
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"1": "Real" |
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} |
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def classify_image(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=2, label="Deepfake Detection"), |
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title="open-deepfake-detection", |
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description="Upload an image to detect whether it is AI-generated (Fake) or a real photograph (Real), using the OpenDeepfake-Preview dataset." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## Demo Inference |
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> [!warning] |
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real |
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> [!warning] |
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fake |
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## Intended Use |
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`open-deepfake-detection` is designed for: |
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* **Deepfake Detection** – Identify AI-generated or manipulated images. |
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* **Content Moderation** – Flag synthetic or fake visual content. |
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* **Dataset Curation** – Remove synthetic samples from mixed datasets. |
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* **Visual Authenticity Verification** – Check the integrity of visual media. |
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* **Digital Forensics** – Support image source verification and traceability. |
