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Mirage-Photo-Classifier

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

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The model categorizes images into two classes:

• Class 0: Real
• Class 1: Fake

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Run with Transformers 🤗

!pip install -q transformers torch pillow gradio

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()

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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.

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 12 items • Updated 1 day ago • 2