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README.md

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----
-license: apache-2.0
----
+---
+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 [email protected].

config.json

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+{
+  "architectures": [
+    "SiglipModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "siglip",
+  "text_config": {
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "model_type": "siglip_text_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0.dev0",
+  "vision_config": {
+    "hidden_size": 1152,
+    "image_size": 384,
+    "intermediate_size": 4304,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 14
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea2abad2b7f8a9c1aa5e49a244d5d57ffa71c56f720c94bc5d240ef4d6e1d94a
+size 3511950624

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

special_tokens_map.json

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+{
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "1": {
+      "content": "</s>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids"
+  ],
+  "model_max_length": 64,
+  "pad_token": "</s>",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SiglipTokenizer",
+  "unk_token": "<unk>"
+}