SafeMARC - Sensitive Media Automatic Redaction and Concealment

Warning

Pre-Alpha Stage Notice: This project is currently in a pre-alpha stage, and is pretty barebones. No official release build is shipped yet. This project should only be used for development or testing purposes.

SafeMARC is a privacy-focused desktop application designed to automatically detect and redact Sensitive Personal Identifiable Information (SPII) and faces from images, PDFs, and digital documents.

The system will focus on reliably detecting structured sensitive data such as phone numbers, ID numbers, card numbers, and other pattern-based information, along with face detection in visual media.

For categories that require heavier linguistic or AI-based processing (e.g., name detection), the system will provide a customizable rule-based module. Users can define their own redaction rules using keywords, strings, or patterns (e.g., regex), allowing flexible and user-controlled detection without requiring complex model training.

The goal of SafeMARC is to provide a practical, efficient, and user-configurable privacy tool for securely sharing digital content, which runs directly on their system locally.

Setup for Windows

1. Install Python 3.12: Make sure "Add to PATH" is checked.
2. Install Tesseract OCR:
   • Download the installer here: https://github.com/UB-Mannheim/tesseract/wiki
   • IMPORTANT: During install, copy the path (usually C:\Program Files\Tesseract-OCR).
   • You might need to add this path to your System Environment Variables.
3. Create Virtual Environment (CRITICAL): Open your terminal in the project folder and run:

 python -m venv .venv
 .venv\Scripts\activate

4. Install Dependencies:

   pip install -r requirements.txt

5. Run the app:

python main.py

Required Models

Body Detection (Full Body mode)

Download into assets/ directory as efficientdet_lite2.tflite:

curl -L -o assets/efficientdet_lite2.tflite "https://storage.googleapis.com/mediapipe-models/object_detector/efficientdet_lite2/float32/latest/efficientdet_lite2.tflite"

Face Recognition (Blacklist/Whitelist identity matching)

Download OpenCV's SFace deep learning model (~37MB) into assets/:

curl -L -o assets/face_recognition_sface_2021dec.onnx "https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx"

Note

Without the SFace model, face identity matching falls back to LBPH (less accurate). The model is gitignored and must be downloaded separately.

Structure

SafeMARC/
├── .gitignore      
├── requirements.txt
├── README.md
├── setup.py
├── main.py                <-- Entry point of the application
│
├── src/
│   ├── cli/               <-- Command Line Interface logic
|   |   ├── cli.py
│   ├── core/              <-- OCR, Redaction, and Scanning logic
│   │   ├── detectors/     <-- Face & text detection algorithms
│   │   ├── batch_processor.py
│   │   ├── identity_manager.py <-- Face identity recognition (SFace/LBPH)
│   │   ├── redactor.py
│   │   ├── scanner.py
│   │   └── types.py
│   ├── gui/               <-- PySide6-based Graphical User Interface
│   │   ├── main_window.py <-- Main application window layout & workflows
│   │   ├── preview_widget.py <-- Interactive image preview, zoom & draw area
│   │   └── settings_dialog.py <-- Settings & Identity Manager UI
│   └── utils/             <-- Helpers (file path normalizers, conversions)
│       └── pdf_handler.py <-- PDF page extraction and rasterized rebuilding
│
├── assets/                <-- Icons, logos, UI themes, AI models
│   └── identities/        <-- Reference face images per person
├── test_data/             <-- Sample images and PDFs for testing
├── tests/                 <-- Unit and integration tests
└── docs/                  <-- Project and developer documentation
    ├── features.md        <-- Project feature roadmap & status
    ├── shortcuts.md       <-- Keyboard and mouse shortcut guide
    ├── ui_guidelines.md   <-- Theme, colors, and layout guidelines
    └── architecture/      <-- UML, use cases, and workflow diagrams
        ├── uml_classes.md
        ├── use_cases.md
        └── workflows.md