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https://github.com/donartkins/deepcheckai

DeepCheck AI is a comprehensive Python-based service for detecting manipulated media including images, videos, and audio files
https://github.com/donartkins/deepcheckai

cloudinary-api deepfake-detection flask-api python3 rest-api

Last synced: 10 months ago
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DeepCheck AI is a comprehensive Python-based service for detecting manipulated media including images, videos, and audio files

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README 

          
# DeepCheck AI 🛡️



[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)



DeepCheck AI is a comprehensive Python-based service for detecting manipulated media including **images, videos, and audio files**. Built with a modular architecture, it integrates state-of-the-art deepfake detection models and provides easy-to-use REST API endpoints that can be seamlessly connected to web frontends.



---



## ✨ Key Features



- 🖼️ **Multi-Modal Detection**: Analyze images, videos, and audio files

- 📊 **Confidence Scoring**: Get detailed confidence metrics for all results

- 📁 **Bulk Processing**: Upload and process multiple files simultaneously

- 🔧 **Modular Architecture**: Easy integration of new detection models

- 🌐 **REST API**: Ready-to-use Flask/FastAPI endpoints

- ⚙️ **Configurable**: Flexible configuration via environment variables

- 🚀 **Production Ready**: Optimized for deployment and scaling



---



## 📂 Project Structure



```

deepfake-ai-service/

├── app/

│   ├── main.py                 # Application entry point

│   ├── models/                 # AI detection models

│   │   ├── image_detector.py   # Image deepfake detection

│   │   ├── video_detector.py   # Video deepfake detection

│   │   └── audio_detector.py   # Audio deepfake detection

│   ├── utils/                  # Utility functions

│   │   ├── preprocessing.py    # Media preprocessing

│   │   ├── validation.py       # Input validation

│   │   └── helpers.py          # Common helper functions

│   └── routes/                 # API route definitions

│       ├── api.py              # Main API routes

│       └── health.py           # Health check endpoints

├── config/

│   ├── settings.py             # Application settings

│   └── logging.conf            # Logging configuration

├── tests/                      # Test suite

│   ├── test_models.py          # Model tests

│   ├── test_api.py             # API endpoint tests

│   └── fixtures/               # Test media files

├── temp/                       # Temporary file storage

├── logs/                       # Application logs

├── requirements.txt            # Python dependencies

├── requirements-dev.txt        # Development dependencies

├── .env.example                # Environment variables template

├── .gitignore                  # Git ignore rules

├── Dockerfile                  # Docker configuration

├── docker-compose.yml          # Docker Compose setup

├── run.py                      # Application runner

├── README.md                   # This file

└── CONTRIBUTING.md             # Contribution guidelines

```



---



## 🚀 Quick Start



### Prerequisites



- Python 3.8 or higher

- Git

- Virtual environment (recommended)



### Installation



1. **Clone the repository**



   ```bash

   git clone https://github.com/YOUR_USERNAME/deepcheck-ai.git

   cd deepcheck-ai

   ```



2. **Create and activate virtual environment**



   ```bash

   # On macOS/Linux

   python3 -m venv venv

   source venv/bin/activate



   # On Windows

   python -m venv venv

   venv\Scripts\activate

   ```



3. **Install dependencies**



   ```bash

   pip install -r requirements.txt

   ```



4. **Configure environment**



   ```bash

   cp .env.example .env

   # Edit .env with your configuration

   ```



5. **Run the application**

   ```bash

   python run.py

   ```



The service will be available at `http://localhost:5000`



---



## 🖥️ Usage



### API Endpoints



#### Health Check



```bash

GET /health

```



#### Upload and Analyze Media



```bash

POST /api/analyze

Content-Type: multipart/form-data



# Parameters:

# - file: Media file (image/video/audio)

# - threshold: Confidence threshold (optional, default: 0.5)

```



#### Example Response



```json

{

  "status": "success",

  "file_type": "image",

  "is_deepfake": false,

  "confidence": 0.85,

  "processing_time": 2.34,

  "metadata": {

    "model_version": "v2.1.0",

    "timestamp": "2024-03-15T10:30:00Z"

  }

}

```



### Python Client Example



```python

import requests



url = "http://localhost:5000/api/analyze"

files = {"file": open("suspicious_image.jpg", "rb")}

data = {"threshold": 0.7}



response = requests.post(url, files=files, data=data)

result = response.json()



print(f"Is deepfake: {result['is_deepfake']}")

print(f"Confidence: {result['confidence']}")

```



### Frontend Integration



The API is designed to work seamlessly with web frontends. For a complete example with Next.js, check out our [frontend repository](https://github.com/YOUR_USERNAME/deepcheck-frontend).



---



## 🔧 Configuration



### Environment Variables



Create a `.env` file in the root directory:



```env

# Server Configuration

PORT=5000

DEBUG=True

SECRET_KEY=your-secret-key-here



# Model Configuration

DEFAULT_THRESHOLD=0.5

MAX_FILE_SIZE=100MB

SUPPORTED_FORMATS=jpg,jpeg,png,mp4,avi,wav,mp3



# Storage Configuration

TEMP_DIR=./temp

LOG_LEVEL=INFO



# GPU Configuration (optional)

USE_GPU=True

CUDA_DEVICE=0

```



### Advanced Configuration



For advanced settings, modify `config/settings.py`:



```python

class Config:

    # Model configurations

    IMAGE_MODEL_PATH = "models/image_detector.pth"

    VIDEO_MODEL_PATH = "models/video_detector.pth"

    AUDIO_MODEL_PATH = "models/audio_detector.pth"



    # Processing limits

    MAX_VIDEO_DURATION = 300  # seconds

    MAX_BATCH_SIZE = 10



    # Performance tuning

    WORKER_THREADS = 4

    ENABLE_CACHING = True

```



---



## 🐳 Docker Deployment



### Using Docker Compose (Recommended)



```bash

docker-compose up -d

```



### Manual Docker Build



```bash

# Build image

docker build -t deepcheck-ai .



# Run container

docker run -p 5000:5000 -v $(pwd)/temp:/app/temp deepcheck-ai

```



---



## 🧪 Testing



Run the test suite:



```bash

# Install development dependencies

pip install -r requirements-dev.txt



# Run tests

python -m pytest tests/ -v



# Run with coverage

python -m pytest tests/ --cov=app --cov-report=html

```



---



## 📈 Performance



### Benchmarks



| Media Type | Average Processing Time     | GPU Acceleration |

| ---------- | --------------------------- | ---------------- |

| Images     | 0.5s                        | 3x faster        |

| Videos     | 2-10s (depending on length) | 5x faster        |

| Audio      | 1-3s                        | 2x faster        |



### Optimization Tips



- Enable GPU acceleration for significant performance gains

- Use batch processing for multiple files

- Configure appropriate worker threads based on your hardware

- Enable caching for repeated analyses



---



## 🛣️ Roadmap



### Version 2.0



- [ ] Advanced transformer-based models (BERT, Vision Transformer)

- [ ] Real-time streaming detection

- [ ] Enhanced GPU optimization (TensorRT integration)

- [ ] Multi-language support for API documentation



### Version 2.1



- [ ] Kubernetes deployment templates

- [ ] Advanced analytics dashboard

- [ ] Model fine-tuning capabilities

- [ ] Integration with cloud storage providers (AWS S3, Google Cloud)



### Version 3.0



- [ ] Federated learning support

- [ ] Custom model training interface

- [ ] Advanced forensic analysis features

- [ ] Mobile SDK for iOS/Android



---



## 🤝 Contributing



We welcome contributions from developers of all skill levels! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines on how to contribute to DeepCheck AI.



### Quick Contribution Steps



1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request



---



## 📄 License



This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.



---



## 🙏 Acknowledgements



We thank the following projects and research communities:



- **[DeepFace](https://github.com/serengil/deepface)** - Face recognition and analysis framework

- **[OpenCV](https://opencv.org/)** - Computer vision library

- **[PyTorch](https://pytorch.org/)** - Deep learning framework

- **[Librosa](https://librosa.org/)** - Audio analysis library

- **Academic Research Community** - For groundbreaking work in deepfake detection



### Research Papers



- "FaceForensics++: Learning to Detect Manipulated Facial Images" (Rössler et al., 2019)

- "The DeepFake Detection Challenge (DFDC) Dataset" (Dolhansky et al., 2020)

- "DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection" (Jiang et al., 2020)



---



## 📞 Support



- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/YOUR_USERNAME/deepcheck-ai/issues)

- 💡 **Feature Requests**: [GitHub Discussions](https://github.com/YOUR_USERNAME/deepcheck-ai/discussions)

- 📧 **Email**: support@deepcheck-ai.com

- 💬 **Discord**: [Join our community](https://discord.gg/deepcheck-ai)



---



## 📊 Project Stats



![GitHub stars](https://img.shields.io/github/stars/YOUR_USERNAME/deepcheck-ai?style=social)

![GitHub forks](https://img.shields.io/github/forks/YOUR_USERNAME/deepcheck-ai?style=social)

![GitHub issues](https://img.shields.io/github/issues/YOUR_USERNAME/deepcheck-ai)

![GitHub pull requests](https://img.shields.io/github/issues-pr/YOUR_USERNAME/deepcheck-ai)



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