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## 🔭 Model Overview


**Satyadrishti-V1** is India’s First Open-Source Deepfake Detection engine by [AIRAS INC](https://www.airas.ai). It is designed to detect image authenticity by classifying images as either "Real" or "Fake". This model is part of the Satyadrishti series and leverages Google's `siglip2-base-patch16-224` vision-language architecture.

This specific variant, **Satyadrishti-V1 Small**, has been fine-tuned for lightweight deployment while maintaining high accuracy.

---

## 📦 General Information

| Metadata | Value |
|---------|-------|
| **Model Name** | Satyadrishti-V1 Small |
| **Model ID** | AirasInnovations/Satyadrishti-V1-SMALL |
| **License** | Apache-2.0 |
| **Language** | English |
| **Library** | Transformers |
| **Base Model** | google/siglip2-base-patch16-224 |
| **Pipeline Tag** | image-classification |
| **Tags** | deepfakedetection, image-authenticity |

---

## 🧠 Model Architecture

- **Architecture**: Fine-tuned SigLIP Vision Transformer
- **Task**: Binary Image Classification (Real vs Fake)
- **Classes**:
  - `Class 0`: Fake (Deepfake / AI-generated / Manipulated)
  - `Class 1`: Real (Authentic)

---

## 📈 Training Details

### Dataset
- **Primary Dataset**: [prithivMLmods/Deepfake-vs-Real-v2](https://huggingface.co/datasets/prithivMLmods/Deepfake-vs-Real-v2)
- **Image Count**: ~5.47k
- **Image Size**: Up to 194px width
- **Includes**: Real photos and synthetic images including outputs from DALL-E 3, Imagen, etc.
- **Data Augmentation**: Horizontal flips, rotations, color jittering

### Hyperparameters
| Parameter | Value |
|---------|-------|
| **Base Model** | google/siglip2-base-patch16-224 (~375M params) |
| **Fine-Tuned Model Size** | ~92 million parameters |
| **Optimizer** | AdamW |
| **Learning Rate** | 5e-5 |
| **Loss Function** | Cross-Entropy Loss |
| **Epochs** | 10 |
| **Hardware** | 2x NVIDIA T4 GPUs |
| **Training Time** | ~4 hours |

---

## ✅ Evaluation Metrics

| Metric | Score |
|--------|-------|
| **Accuracy** | 96% |
| **Precision** | 98% |
| **Recall** | 98% |
| **F1-Score** | 97% |

> ⚠️ Note: These metrics were computed on a dedicated validation set. Performance may vary depending on real-world input distribution and image quality.

---

## 📌 Intended Use

The model is intended for detecting whether an image is **real (authentic)** or **fake (manipulated or AI-generated)**. It can be applied in various domains:

- **Social Media Moderation**
- **Digital Forensics**
- **Journalism & Fact-Checking**
- **Authentication Systems**
- **Research & Development**

---

## 🛠️ How to Use

### Requirements

```bash
pip install transformers torch pillow gradio
```

### Code Example

```python
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

model_name = "AirasInnovations/Satyadrishti-V1-SMALL"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def detect_authenticity(image):
    image = image.convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        logits = model(**inputs).logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
    labels = model.config.id2label
    return {labels[i]: round(probs[i], 3) for i in range(len(probs))}
```

You can also use it directly via Gradio interface as shown in the example script.

---

## 🆚 Comparison with Vatsav Deepfake Detection

| Feature | Vatsav Deepfake Detection | Satyadrishti-V1 Small |
|--------|----------------------------|------------------------|
| **Training Data Age** | 3 years old | Jan–April 2025 |
| **Evaluation Data** | Kaggle dataset | prithivMLmods Deepfake-vs-Real-v2 |
| **Open Source?** | ❌ No | ✅ Yes |
| **Accuracy** | 75% | 96% |
| **Precision** | 75% | 98% |
| **Recall** | 100% | 98% |
| **F1-Score** | ~86% | 97% |

---

## 📚 Citation & Acknowledgments

We thank [Google Research](https://ai.googleblog.com/) for open-sourcing the SigLIP architecture and [Prithiv MLMods](https://huggingface.co/prithivMLmods) for providing updated datasets for training.

If you use this model in your work, please cite:

```
@misc{airas-satyadrishti,
  author = {AIRAS INC},
  title = {Satyadrishti: India's First Open-Source Deepfake Detection Engine},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Model Card},
  howpublished = {\url{https://huggingface.co/AirasInnovations/Satyadrishti-V1-SMALL}}
}
```

---

## 📬 Contact

For support, feedback, or collaboration opportunities, please visit [AIRAS INC Website](https://www.airas.ai) or reach out to us at contact@airas.ai.
fake real