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---
license: apache-2.0
datasets:
- anson-huang/mirage-news
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Fake
- Real
- SigLIP2
- Mirage
---
# **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.
```py
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 🤗**
```python
!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. |