prithivMLmods / deepfake-detector-model-v1

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.

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

Label Space: 2 Classes

The model classifies an image as one of the following:

Class 0: fake  
Class 1: real

Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

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()

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.