Mirage-Photo-Classifier is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a binary image authenticity classification task. It is designed to determine whether an image is real or AI-generated (fake) using the SiglipForImageClassification architecture.

Classification Report:
              precision    recall  f1-score   support

        Real     0.9781    0.9132    0.9446      5000
        Fake     0.9186    0.9796    0.9481      5000

    accuracy                         0.9464     10000
   macro avg     0.9484    0.9464    0.9463     10000
weighted avg     0.9484    0.9464    0.9463     10000

The model categorizes images into two classes:

Class 0: Real
Class 1: Fake

Run with Transformers 🤗

!pip install -q transformers torch pillow gradio

python

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Mirage-Photo-Classifier"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
labels = {
    "0": "Real",
    "1": "Fake"
}

def classify_image_authenticity(image):
    """Predicts whether the image is real or AI-generated (fake)."""
    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()

    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    
    return predictions

# Gradio interface
iface = gr.Interface(
    fn=classify_image_authenticity,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="Mirage Photo Classifier",
    description="Upload an image to determine if it's Real or AI-generated (Fake)."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()

Intended Use

The Mirage-Photo-Classifier model is designed to detect whether an image is genuine (photograph) or synthetically generated. Use cases include:

AI Image Detection: Identifying AI-generated images in social media, news, or datasets.
Digital Forensics: Helping professionals detect image authenticity in investigations.
Platform Moderation: Assisting content platforms in labeling generated content.
Dataset Validation: Cleaning and verifying training data for other AI models.