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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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--- |
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# **AI-vs-Deepfake-vs-Real-Siglip2** |
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**AI-vs-Deepfake-vs-Real-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to distinguish AI-generated images, deepfake images, and real images using the SiglipForImageClassification architecture. |
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The model categorizes images into three classes: |
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- **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models. |
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- **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered. |
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- **Class 2:** "Real" – The image is an authentic, unaltered photograph. |
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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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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/AI-vs-Deepfake-vs-Real-Siglip2" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def image_classification(image): |
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"""Classifies an image as AI-generated, deepfake, or real.""" |
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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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labels = model.config.id2label |
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predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=image_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Classification Result"), |
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title="AI vs Deepfake vs Real Image Classification", |
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description="Upload an image to determine whether it is AI-generated, a deepfake, or a real image." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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Classification report: |
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precision recall f1-score support |
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AI 0.9794 0.9955 0.9874 1334 |
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Deepfake 0.9931 0.9782 0.9856 1333 |
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Real 0.9992 0.9977 0.9985 1333 |
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accuracy 0.9905 4000 |
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macro avg 0.9906 0.9905 0.9905 4000 |
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weighted avg 0.9906 0.9905 0.9905 4000 |
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# **Intended Use:** |
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The **AI-vs-Deepfake-vs-Real-Siglip2** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image. |
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### Potential Use Cases: |
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- **Deepfake Detection:** Identifying manipulated deepfake content in media. |
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- **AI-Generated Image Identification:** Distinguishing AI-generated images from real or deepfake images. |
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- **Content Verification:** Supporting fact-checking and digital forensics in assessing image authenticity. |
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- **Social Media and News Filtering:** Helping platforms flag AI-generated or deepfake content. |
