README.md

metadata

license: apache-2.0
pipeline_tag: image-classification
library_name: transformers
tags:
  - deep-fake
  - ViT
  - detection
  - Image
base_model:
  - google/vit-base-patch16-224-in21k
datasets:
  - prithivMLmods/OpenDeepfake-Preview
language:
  - en

deepfake-detector-model

deepfake-detector-model is a vision-language model fine-tuned from google/vit-base-patch16-224-in21k 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 ViTForImageClassification architecture.

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Label Space: 2 Classes

The model classifies an image as either:

Class 0: fake  
Class 1: real

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Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

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Inference Code

import gradio as gr
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "your-username/deepfake-detector-model"
model = ViTForImageClassification.from_pretrained(model_name)
processor = ViTImageProcessor.from_pretrained(model_name)

# Updated label mapping
labels_list = ['fake', 'real']

def classify_image(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {
        labels_list[i]: round(probs[i], 3) for i in range(len(probs))
    }

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Deepfake Detection"),
    title="deepfake-detector-model",
    description="Upload an image to detect whether it is AI-generated (fake) or a real photograph (real), using the OpenDeepfake-Preview dataset."
)

if __name__ == "__main__":
    iface.launch()

---

Intended Use

deepfake-detector-model is designed for:

• Deepfake Detection – Identify AI-generated or manipulated images.
• Content Moderation – Flag synthetic or fake visual content.
• Dataset Curation – Remove synthetic samples from mixed datasets.
• Visual Authenticity Verification – Check the integrity of visual media.
• Digital Forensics – Support image source verification and traceability.