dfit-toolkit 1.0.0

A comprehensive command-line forensics toolkit for digital image analysis

🔍 DFIT - Digital Image Forensics Toolkit

A comprehensive command-line forensics toolkit for digital image analysis. Detect tampering, extract hidden data, analyze metadata, and identify steganography.

🎯 Key Features

• Metadata Extraction - EXIF data, GPS coordinates, timestamps, file hashes
• Tampering Detection - Error Level Analysis (ELA) for pixel-level anomalies
• Steganography Detection - LSB analysis and statistical detection
• Hidden Data Extraction - Extract embedded data from images
• Batch Processing - Process entire directories recursively
• Professional Output - Color-coded console, JSON export, HTML reports

📋 Requirements

• Python 3.8+
• PIL/Pillow, OpenCV, NumPy, Click, ExifRead

🚀 Installation

Option 1: Install from PyPI (Recommended)

# Simple installation
pip install dfit-toolkit

# Or with pipx for isolated environment
pipx install dfit-toolkit

# Verify installation
dfit --version

Option 2: Install from Source

git clone https://github.com/C0d3-cr4f73r/DFIT.git
cd DFIT
pip install -e .

# Or with virtual environment
python3 -m venv venv
source venv/bin/activate
pip install -e .

📖 Usage

# Extract metadata
dfit metadata -i image.jpg

# Detect tampering
dfit detect-tampering -i image.jpg

# Scan for steganography
dfit scan-stego -i image.jpg

# Extract hidden data
dfit extract -i image.jpg -o secret.bin

# Comprehensive analysis
dfit analyze -i image.jpg

# Batch processing
dfit batch -i ./images --recursive

# Export results to JSON
dfit metadata -i image.jpg -o report.json

🧪 Testing

python3 -m pytest tests/ -v
# Expected: 23/23 tests passing

🐳 Docker

docker build -t dfit:latest .
docker run --rm dfit:latest --help
docker-compose up --build

📚 Documentation

• CLI Guide - Detailed command reference
• Docker Guide - Docker setup
• Quick Start - Get started in 5 minutes
• Project Summary - Architecture overview

🎓 Real-World Use Cases

• Law Enforcement - Verify evidence authenticity
• Journalism - Verify image authenticity before publication
• Cybersecurity - Analyze malware delivery images
• Insurance & Legal - Verify claim authenticity

📁 Project Structure

src/
├── cli/          # CLI interface
├── core/         # Analysis modules
└── utils/        # Utilities

tests/            # Test suite (23 tests)
docs/             # Documentation

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

📝 License

MIT License - see LICENSE file for details.

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Made with ❤️ for digital forensics professionals