---
license: apache-2.0
language:
- en
base_model:
- google/siglip-so400m-patch14-384
pipeline_tag: image-classification
library_name: transformers
tags:
- deepfakedetection
- image-authenticity
- image-classification
---
![bXfKBT3LQkbeLzPCBHTGT.png](https://cas-bridge.xethub.hf.co/xet-bridge-us/68206a8f14f1230be619fde3/27a2411a7aabcf28ea23c565d031a53b7e5e2fca4c0dfc17cbc0ed3fb32ec2fa?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20250511%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250511T104120Z&X-Amz-Expires=3600&X-Amz-Signature=fb917f3ab607b8042bdf3c6a95fec4315d23d907d1705aef14cfdda11532dab7&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=675d3ca88d0f15d76e49d5ea&response-content-disposition=inline%3B+filename*%3DUTF-8%27%27Gemini_Generated_Image_3sc2d03sc2d03sc2.jpeg%3B+filename%3D%22Gemini_Generated_Image_3sc2d03sc2d03sc2.jpeg%22%3B&response-content-type=image%2Fjpeg&x-id=GetObject&Expires=1746963680&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0Njk2MzY4MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2FzLWJyaWRnZS54ZXRodWIuaGYuY28veGV0LWJyaWRnZS11cy82ODIwNmE4ZjE0ZjEyMzBiZTYxOWZkZTMvMjdhMjQxMWE3YWFiY2YyOGVhMjNjNTY1ZDAzMWE1M2I3ZTVlMmZjYTRjMGRmYzE3Y2JjMGVkM2ZiMzJlYzJmYSoifV19&Signature=TkLl8ndIUD3coiaY3Ujgj98hdF2K0DVRn4L2LH1SMCZ6NVSaoDsWkgv5jOlGkzp-8Gwv1240BeUkNqJ759UWZY6%7Egr4DJMxQVR15pW0OCTwbWlj6XzT2UgEtYyWwxMfN5yeirKdXAvbPVikaUjUEcqYOu7sshvu7fK7W4iTaap2FuRVt%7EhqPpjRBiGrPRMCrZmY1jpwNHzu34iUhbfg4uWngoXuobviWgBhfXzBG-0GvLf%7EOFwNsYOq3PHYtz9kDeKiNLCaMx0unxke8FHSjzquQ6bnD0uNltmnN-aXZjSiNC-qW4U-%7EQeocdZybMglZiKq1%7E%7Eb5LDnOsG2cafNWSA__&Key-Pair-Id=K2L8F4GPSG1IFC)
### Satyadrishti-V1 Large
## 🔭 Model Overview

**Satyadrishti-V1** is an open-source image classification model by [AIRAS INC](https://www.airas.ai), fine-tuned for deepfake detection. It is designed to classify images, determining if they are "Real" or "Fake". This model is part of the Satyadrishti series and leverages Google's `google/siglip-so400m-patch14-384` image classification architecture.

This specific variant, **Satyadrishti-V1 Large**, has been fine-tuned for robust performance with its 840 million parameters.

---

## 📦 General Information

| Metadata | Value |
|---------|-------|
| **Model Name** | Satyadrishti-V1 Large |
| **Model ID** | AirasInnovations/Satyadrishti-V1-LARGE |
| **License** | Apache-2.0 |
| **Language** | English |
| **Library** | Transformers |
| **Base Model** | google/siglip-so400m-patch14-384 |
| **Pipeline Tag** | image-classification |
| **Tags** | image-classification, 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
- **Datasets**: Utilizes over 50 diverse datasets, including selections from prithivMLmods, Kaggle, and other sources.
- **Content**: Includes a wide variety of real photos and synthetic images, covering outputs from models like DALL-E 3, Imagen, and others.
- **Data Augmentation**: Horizontal flips, rotations, color jittering

### Hyperparameters
| Parameter | Value |
|---------|-------|
| **Base Model** | google/siglip-so400m-patch14-384 (~400M params) |
| **Fine-Tuned Model Size** | ~840 million parameters |
| **Optimizer** | AdamW |
| **Learning Rate** | 5e-5 |
| **Loss Function** | Cross-Entropy Loss |
| **Epochs** | 10 |
| **Hardware** | 2x NVIDIA T4 GPUs |
| **Training Time** | ~36 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-LARGE"
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 Large |
|--------|----------------------------|------------------------|
| **Training Data Age** | 3 years old | Jan–April 2025 |
| **Evaluation Data** | Kaggle dataset | Multiple datasets including 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-large,
  author = {AIRAS INC},
  title = {Satyadrishti-V1 Large: An Open-Source Image Classification Model for Deepfake Detection},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Model Card},
  howpublished = {\url{https://huggingface.co/AirasInnovations/Satyadrishti-V1-LARGE}}
}
```

---

## 📬 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.