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deepfake-detector-model-v1

deepfake-detector-model-v1 is a vision-language encoder model fine-tuned from google/siglip-base-patch16-512 for binary deepfake image classification. It is trained to detect whether an image is real or generated using synthetic media techniques. The model uses the SiglipForImageClassification architecture.

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Experimental

Classification Report:
              precision    recall  f1-score   support

        Fake     0.9718    0.9155    0.9428     10000
        Real     0.9201    0.9734    0.9460      9999

    accuracy                         0.9444     19999
   macro avg     0.9459    0.9444    0.9444     19999
weighted avg     0.9459    0.9444    0.9444     19999

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Label Space: 2 Classes

The model classifies an image as one of the following:

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 AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/deepfake-detector-model-v1"  
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated label mapping
id2label = {
    "0": "fake",
    "1": "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 = {
        id2label[str(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 Classification"),
    title="deepfake-detector-model",
    description="Upload an image to classify whether it is real or fake using a deepfake detection model."
)

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

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Intended Use

deepfake-detector-model is designed for:

• Deepfake Detection – Accurately identify fake images generated by AI.
• Media Authentication – Verify the authenticity of digital visual content.
• Content Moderation – Assist in filtering synthetic media in online platforms.
• Forensic Analysis – Support digital forensics by detecting manipulated visual data.
• Security Applications – Integrate into surveillance systems for authenticity verification.