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---
license: mit
datasets:
- Parveshiiii/AI-vs-Real
base_model:
- microsoft/swinv2-tiny-patch4-window16-256
pipeline_tag: image-classification
library_name: transformers
tags:
- safety
- XenArcAI
- SoTA
---
# XenArcAI

<p align="center">
  <img 
    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/m7ddTjuxxLUntdXVk0t5N.png" 
    alt="AIRealNet Banner" 
    width="90%" 
    style="border-radius:15px;"
  />
</p>

---

- [GitHub Repository](https://github.com/XenArcAI/AIRealNet)
- [Live Demo](https://huggingface.co/spaces/Parveshiiii/AIRealNet)
  
## Overview

In an era of rapidly advancing AI-generated imagery, deepfakes, and synthetic media, the need for reliable detection tools has never been higher. **AIRealNet** is a binary image classifier explicitly designed to distinguish **AI-generated images** from **real human photographs**. This model is optimized to detect conventional AI-generated content while adhering to strict privacy standards—avoiding personal or sensitive images.

* **Class 0:** AI-generated image
* **Class 1:** Real human image

By leveraging the robust **SwinV2 Tiny** architecture as its backbone, AIRealNet achieves a high degree of accuracy while remaining lightweight enough for practical deployment.

---

## Key Features

1. **High Accuracy on Public Datasets:**
   Despite using a **14k-image fine-tuning split(Part of main fine tuning split)**, AIRealNet demonstrates exceptional accuracy and robustness in detecting AI-generated images.

2. **Balanced Training Split:**
   The dataset contains a balanced number of AI-generated and real images, ensuring unbiased training and minimizing class imbalance issues.

   * **AI-Generated:** 60%
   * **Human-Images:** 40%

4. **Ethical Design:**
   No personal photos were included, even if edited or AI-modified, respecting privacy and ethical AI principles.

5. **Fast and Scalable:**
   Based on a transformer vision model, AIRealNet can be deployed efficiently in both research and production environments.

---

## Training Data

* **Dataset:** `Parveshiiii/AI-vs-Real` (open-sourced subset of main dataset )
* **Size:** 14k images (balanced between AI and human)
* **Split:** Used the train split for fine-tuning; validation performed on a separate balanced subset.
* **Notes:** Images sourced from public datasets and AI generation tools. Edited personal photos were intentionally excluded.

---

## Limitations

While AIRealNet performs exceptionally well on typical AI-generated images, users should note:

1. **Subtle Edits:** The model struggles with nano-scale edits or ultra-precise modifications, like “nano banana” edits.
2. **Edited Personal Images(over precise):** Images of real people that have been AI-modified are **not detected**, aligning with privacy and ethical guidelines.
3. **Domain Generalization:** Performance may vary on images from completely unseen AI generators or extremely unconventional content.

---

## Performance Metrics

> Metrics shown are from **Epoch 2**, chosen to illustrate stable performance after fine-tuning.

<p align="center">
  <img 
    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/3NVa0KLX0iAxTP2e6IlGH.png" 
    alt="AIRealNet Banner" 
    width="90%" 
    style="border-radius:15px;"
  />
</p>

**Note:** Extremely low loss and high accuracy are due to the controlled dataset environment. Real-world performance may be lower depending on the image domain.(In our testing this is model is over accurate despite it can't detect Nano-Banana images(only edited fully generated images can be detected over accurately))

---

## Demo and Usage

1. **Installing dependecies**

```python
pip install -U transformers
```
2. **Loading and running a demo**

```python
from transformers import pipeline

pipe = pipeline("image-classification", model="XenArcAI/AIRealNet")
pipe("https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/eVkKUTdiInUl6pbIUghQC.png")# example image
```
# Demo

* **Given Image**(Checkout Maths best filtered dataset focused on reasoning on XenArcAI)

<p align="center">
  <img 
    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/eVkKUTdiInUl6pbIUghQC.png" 
    alt="AIRealNet Banner" 
    width="90%" 
    style="border-radius:15px;"
  />
</p>

* **Model Output**

```bash
[{'label': 'artificial', 'score': 0.9865425825119019},
 {'label': 'real', 'score': 0.013457471504807472}]
```
**Note:** its correct as the image was generated by a diffusion model

---

## Intended Use

* Detect AI-generated imagery on social media, research publications, and digital media platforms.
* Assist content moderators, researchers, and fact-checkers in identifying synthetic media.
* **Not intended** for legal verification without human corroboration.

---

## Ethical Considerations

* **Privacy-first Approach:** Personal photos, even if AI-edited, were excluded.
* **Responsible Deployment:** Users should combine model predictions with human review to avoid false positives or negatives.
* **Transparency:** The model card openly communicates its limitations and dataset design to prevent misuse.

---

## How It Works

1. Images are preprocessed and resized to `256x256`.
2. Features are extracted using the **SwinV2 Tiny** vision transformer backbone.
3. A binary classification head outputs probabilities for AI-generated vs real human images.
4. Predictions are interpreted as class 0 (AI) or class 1 (Human).

---

## Future Work

Future iterations aim to:

* Improve detection of subtle AI-generated edits and “nano banana” modifications.
* Expand training data with diverse AI generators to enhance generalization.
* Explore multi-modal detection capabilities (e.g., video, metadata, and image combined).

---

### Citation
```bibtex
@misc{xenarcai_sparkembedding_2025,
    title={SparkEmbedding-300m: A Fine-Tuned Multilingual Embedding Model for Cross-Lingual Retrieval},
    author={Parvesh Rawal},
    publisher={Hugging Face},
    year={2025},
    url={https://huggingface.co/XenArcAI/AIRealNet}
}
```

## References

* Microsoft SwinV2 Tiny: [https://github.com/microsoft/Swin-Transformer](https://github.com/microsoft/Swin-Transformer)
* Parveshiiii/AI-vs-Real dataset (subset): Open-sourced by our team member

---