{"id":31056445,"url":"https://github.com/donartkins/deepcheckai","last_synced_at":"2025-09-15T05:21:04.948Z","repository":{"id":314101025,"uuid":"1054158900","full_name":"DonArtkins/deepcheckai","owner":"DonArtkins","description":"DeepCheck AI is a comprehensive Python-based service for detecting manipulated media including images, videos, and audio files","archived":false,"fork":false,"pushed_at":"2025-09-10T13:12:43.000Z","size":18,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-10T17:17:43.557Z","etag":null,"topics":["cloudinary-api","deepfake-detection","flask-api","python3","rest-api"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DonArtkins.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-10T12:55:52.000Z","updated_at":"2025-09-10T13:17:10.000Z","dependencies_parsed_at":"2025-09-10T17:28:23.390Z","dependency_job_id":null,"html_url":"https://github.com/DonArtkins/deepcheckai","commit_stats":null,"previous_names":["donartkins/deepcheckai"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/DonArtkins/deepcheckai","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DonArtkins%2Fdeepcheckai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DonArtkins%2Fdeepcheckai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DonArtkins%2Fdeepcheckai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DonArtkins%2Fdeepcheckai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DonArtkins","download_url":"https://codeload.github.com/DonArtkins/deepcheckai/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DonArtkins%2Fdeepcheckai/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275207875,"owners_count":25423896,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-15T02:00:09.272Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cloudinary-api","deepfake-detection","flask-api","python3","rest-api"],"created_at":"2025-09-15T05:21:02.783Z","updated_at":"2025-09-15T05:21:04.913Z","avatar_url":"https://github.com/DonArtkins.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DeepCheck AI 🛡️\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\nDeepCheck 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.\n\n---\n\n## ✨ Key Features\n\n- 🖼️ **Multi-Modal Detection**: Analyze images, videos, and audio files\n- 📊 **Confidence Scoring**: Get detailed confidence metrics for all results\n- 📁 **Bulk Processing**: Upload and process multiple files simultaneously\n- 🔧 **Modular Architecture**: Easy integration of new detection models\n- 🌐 **REST API**: Ready-to-use Flask/FastAPI endpoints\n- ⚙️ **Configurable**: Flexible configuration via environment variables\n- 🚀 **Production Ready**: Optimized for deployment and scaling\n\n---\n\n## 📂 Project Structure\n\n```\ndeepfake-ai-service/\n├── app/\n│   ├── main.py                 # Application entry point\n│   ├── models/                 # AI detection models\n│   │   ├── image_detector.py   # Image deepfake detection\n│   │   ├── video_detector.py   # Video deepfake detection\n│   │   └── audio_detector.py   # Audio deepfake detection\n│   ├── utils/                  # Utility functions\n│   │   ├── preprocessing.py    # Media preprocessing\n│   │   ├── validation.py       # Input validation\n│   │   └── helpers.py          # Common helper functions\n│   └── routes/                 # API route definitions\n│       ├── api.py              # Main API routes\n│       └── health.py           # Health check endpoints\n├── config/\n│   ├── settings.py             # Application settings\n│   └── logging.conf            # Logging configuration\n├── tests/                      # Test suite\n│   ├── test_models.py          # Model tests\n│   ├── test_api.py             # API endpoint tests\n│   └── fixtures/               # Test media files\n├── temp/                       # Temporary file storage\n├── logs/                       # Application logs\n├── requirements.txt            # Python dependencies\n├── requirements-dev.txt        # Development dependencies\n├── .env.example                # Environment variables template\n├── .gitignore                  # Git ignore rules\n├── Dockerfile                  # Docker configuration\n├── docker-compose.yml          # Docker Compose setup\n├── run.py                      # Application runner\n├── README.md                   # This file\n└── CONTRIBUTING.md             # Contribution guidelines\n```\n\n---\n\n## 🚀 Quick Start\n\n### Prerequisites\n\n- Python 3.8 or higher\n- Git\n- Virtual environment (recommended)\n\n### Installation\n\n1. **Clone the repository**\n\n   ```bash\n   git clone https://github.com/YOUR_USERNAME/deepcheck-ai.git\n   cd deepcheck-ai\n   ```\n\n2. **Create and activate virtual environment**\n\n   ```bash\n   # On macOS/Linux\n   python3 -m venv venv\n   source venv/bin/activate\n\n   # On Windows\n   python -m venv venv\n   venv\\Scripts\\activate\n   ```\n\n3. **Install dependencies**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Configure environment**\n\n   ```bash\n   cp .env.example .env\n   # Edit .env with your configuration\n   ```\n\n5. **Run the application**\n   ```bash\n   python run.py\n   ```\n\nThe service will be available at `http://localhost:5000`\n\n---\n\n## 🖥️ Usage\n\n### API Endpoints\n\n#### Health Check\n\n```bash\nGET /health\n```\n\n#### Upload and Analyze Media\n\n```bash\nPOST /api/analyze\nContent-Type: multipart/form-data\n\n# Parameters:\n# - file: Media file (image/video/audio)\n# - threshold: Confidence threshold (optional, default: 0.5)\n```\n\n#### Example Response\n\n```json\n{\n  \"status\": \"success\",\n  \"file_type\": \"image\",\n  \"is_deepfake\": false,\n  \"confidence\": 0.85,\n  \"processing_time\": 2.34,\n  \"metadata\": {\n    \"model_version\": \"v2.1.0\",\n    \"timestamp\": \"2024-03-15T10:30:00Z\"\n  }\n}\n```\n\n### Python Client Example\n\n```python\nimport requests\n\nurl = \"http://localhost:5000/api/analyze\"\nfiles = {\"file\": open(\"suspicious_image.jpg\", \"rb\")}\ndata = {\"threshold\": 0.7}\n\nresponse = requests.post(url, files=files, data=data)\nresult = response.json()\n\nprint(f\"Is deepfake: {result['is_deepfake']}\")\nprint(f\"Confidence: {result['confidence']}\")\n```\n\n### Frontend Integration\n\nThe 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).\n\n---\n\n## 🔧 Configuration\n\n### Environment Variables\n\nCreate a `.env` file in the root directory:\n\n```env\n# Server Configuration\nPORT=5000\nDEBUG=True\nSECRET_KEY=your-secret-key-here\n\n# Model Configuration\nDEFAULT_THRESHOLD=0.5\nMAX_FILE_SIZE=100MB\nSUPPORTED_FORMATS=jpg,jpeg,png,mp4,avi,wav,mp3\n\n# Storage Configuration\nTEMP_DIR=./temp\nLOG_LEVEL=INFO\n\n# GPU Configuration (optional)\nUSE_GPU=True\nCUDA_DEVICE=0\n```\n\n### Advanced Configuration\n\nFor advanced settings, modify `config/settings.py`:\n\n```python\nclass Config:\n    # Model configurations\n    IMAGE_MODEL_PATH = \"models/image_detector.pth\"\n    VIDEO_MODEL_PATH = \"models/video_detector.pth\"\n    AUDIO_MODEL_PATH = \"models/audio_detector.pth\"\n\n    # Processing limits\n    MAX_VIDEO_DURATION = 300  # seconds\n    MAX_BATCH_SIZE = 10\n\n    # Performance tuning\n    WORKER_THREADS = 4\n    ENABLE_CACHING = True\n```\n\n---\n\n## 🐳 Docker Deployment\n\n### Using Docker Compose (Recommended)\n\n```bash\ndocker-compose up -d\n```\n\n### Manual Docker Build\n\n```bash\n# Build image\ndocker build -t deepcheck-ai .\n\n# Run container\ndocker run -p 5000:5000 -v $(pwd)/temp:/app/temp deepcheck-ai\n```\n\n---\n\n## 🧪 Testing\n\nRun the test suite:\n\n```bash\n# Install development dependencies\npip install -r requirements-dev.txt\n\n# Run tests\npython -m pytest tests/ -v\n\n# Run with coverage\npython -m pytest tests/ --cov=app --cov-report=html\n```\n\n---\n\n## 📈 Performance\n\n### Benchmarks\n\n| Media Type | Average Processing Time     | GPU Acceleration |\n| ---------- | --------------------------- | ---------------- |\n| Images     | 0.5s                        | 3x faster        |\n| Videos     | 2-10s (depending on length) | 5x faster        |\n| Audio      | 1-3s                        | 2x faster        |\n\n### Optimization Tips\n\n- Enable GPU acceleration for significant performance gains\n- Use batch processing for multiple files\n- Configure appropriate worker threads based on your hardware\n- Enable caching for repeated analyses\n\n---\n\n## 🛣️ Roadmap\n\n### Version 2.0\n\n- [ ] Advanced transformer-based models (BERT, Vision Transformer)\n- [ ] Real-time streaming detection\n- [ ] Enhanced GPU optimization (TensorRT integration)\n- [ ] Multi-language support for API documentation\n\n### Version 2.1\n\n- [ ] Kubernetes deployment templates\n- [ ] Advanced analytics dashboard\n- [ ] Model fine-tuning capabilities\n- [ ] Integration with cloud storage providers (AWS S3, Google Cloud)\n\n### Version 3.0\n\n- [ ] Federated learning support\n- [ ] Custom model training interface\n- [ ] Advanced forensic analysis features\n- [ ] Mobile SDK for iOS/Android\n\n---\n\n## 🤝 Contributing\n\nWe 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.\n\n### Quick Contribution Steps\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests for new functionality\n5. Submit a pull request\n\n---\n\n## 📄 License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\n## 🙏 Acknowledgements\n\nWe thank the following projects and research communities:\n\n- **[DeepFace](https://github.com/serengil/deepface)** - Face recognition and analysis framework\n- **[OpenCV](https://opencv.org/)** - Computer vision library\n- **[PyTorch](https://pytorch.org/)** - Deep learning framework\n- **[Librosa](https://librosa.org/)** - Audio analysis library\n- **Academic Research Community** - For groundbreaking work in deepfake detection\n\n### Research Papers\n\n- \"FaceForensics++: Learning to Detect Manipulated Facial Images\" (Rössler et al., 2019)\n- \"The DeepFake Detection Challenge (DFDC) Dataset\" (Dolhansky et al., 2020)\n- \"DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection\" (Jiang et al., 2020)\n\n---\n\n## 📞 Support\n\n- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/YOUR_USERNAME/deepcheck-ai/issues)\n- 💡 **Feature Requests**: [GitHub Discussions](https://github.com/YOUR_USERNAME/deepcheck-ai/discussions)\n- 📧 **Email**: support@deepcheck-ai.com\n- 💬 **Discord**: [Join our community](https://discord.gg/deepcheck-ai)\n\n---\n\n## 📊 Project Stats\n\n![GitHub stars](https://img.shields.io/github/stars/YOUR_USERNAME/deepcheck-ai?style=social)\n![GitHub forks](https://img.shields.io/github/forks/YOUR_USERNAME/deepcheck-ai?style=social)\n![GitHub issues](https://img.shields.io/github/issues/YOUR_USERNAME/deepcheck-ai)\n![GitHub pull requests](https://img.shields.io/github/issues-pr/YOUR_USERNAME/deepcheck-ai)\n\n---\n\n\u003cp align=\"center\"\u003e\n  \u003cstrong\u003eBuilt with ❤️ for a safer digital world\u003c/strong\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdonartkins%2Fdeepcheckai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdonartkins%2Fdeepcheckai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdonartkins%2Fdeepcheckai/lists"}