🔍 Veritas - Forensic Image Analysis Toolkit

Veritas is a comprehensive web-based digital forensics tool for detecting image forgeries and manipulations using 13 advanced analysis techniques.

🎯 Features

Core Analysis Techniques

1. Error Level Analysis (ELA) - Multi-quality compression artifact detection
2. Metadata Forensics - EXIF analysis, GPS extraction, thumbnail inconsistencies
3. Histogram Analysis - Statistical color distribution patterns
4. Noise Inconsistency - High-pass filtering for tampered regions
5. JPEG Ghost Detection - Multi-level compression artifacts
6. Quantization Table Analysis - JPEG compression table forensics
7. Copy-Move Forgery Detection (CMFD) - Duplicated region detection
8. PRNU Analysis - Photo Response Non-Uniformity (sensor fingerprints)
9. Frequency Domain Analysis - FFT/DCT-based tampering detection
10. Deepfake Detection - GAN artifact classification
11. Resampling Detection - Image resizing and interpolation artifacts
12. Steganography Detection - LSB statistical analysis for hidden data detection
13. Hash Verification - Cryptographic provenance tracking and authentication

Information Security Features

• 🔐 LSB Steganography Detection - Chi-square testing for hidden data in Least Significant Bits
• 🔑 Blockchain-Based Provenance - Cryptographic and perceptual hash verification
• ⚖️ Legal Chain of Custody - Track image modifications with timestamps
• 🔒 SHA-256 Integrity - Exact file matching for evidence verification
• 👁️ Perceptual Hashing - Detect similar images despite minor modifications

Additional Features

• Technique Descriptions - Built-in educational guides for each analysis method
• Default Sample Image - Preloaded image for instant testing without upload
• Web-based Interface - No installation required, runs in browser
• Dark Theme - Professional forensic UI with neon accents
• 14 Analysis Tabs - Organized, intuitive workflow
• Human-Readable Results - Authenticity scoring (0-100) and risk levels
• Real-time Processing - Instant visual feedback
• Cloud Deployment Ready - Deploy to Streamlit Cloud in minutes

📋 Requirements

• Python 3.9 or higher
• 2GB RAM minimum
• Modern web browser (Chrome, Firefox, Edge)

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/CodeRafay/Forensic-Image-Analysis-Toolkit.git
cd Forensic-Image-Analysis-Toolkit

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate
# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

streamlit run app.py

The application will automatically open in your default browser at http://localhost:8501

📁 Project Structure

VeritasForensics/
├── app.py # Main Streamlit application
├── requirements.txt # Python dependencies
├── requirements-dev.txt # Development dependencies
├── projectSetup.md # Detailed setup guide
├── README.md # This file
├── CHANGELOG.md # Version history
├── CONTRIBUTING.md # Contribution guidelines
├── LICENSE # BSD 3-Clause License
├── pytest.ini # Test configuration
├── .gitignore # Git ignore rules
├── .pre-commit-config.yaml # Pre-commit hooks
├── TECHNIQUE_DESCRIPTIONS_USER_GUIDE.md # User guide for descriptions
│
├── analysis/ # Forensic analysis modules
│ ├── __init__.py
│ ├── ela.py # Error Level Analysis
│ ├── metadata_analysis.py # EXIF + file forensics
│ ├── histogram_analysis.py # Statistical analysis
│ ├── noise_map.py # Noise inconsistency
│ ├── jpeg_ghost.py # Compression artifacts
│ ├── quant_table.py # JPEG quantization
│ ├── cmfd.py # Copy-move detection
│ ├── prnu.py # Sensor fingerprint
│ ├── frequency_analysis.py # FFT/DCT analysis
│ ├── deepfake_detector.py # GAN detection
│ ├── resampling_detector.py # Resampling detection
│ ├── steganography_detection.py # LSB steganography detection
│ ├── hash_verification.py # Cryptographic provenance
│ └── util.py # Helper functions
│
├── Descriptions/ # Technique education module (NEW)
│ ├── ELA.md # ELA guide
│ ├── Metadata.md # Metadata guide
│ ├── Steganography.md # Steganography detection guide
│ ├── Hash_Verification.md # Hash verification guide
│ ├── Histogram.md # Histogram guide
│ ├── Noise_Ghost.md # Noise/Ghost guide
│ ├── Quantization.md # Quantization guide
│ ├── CMFD.md # CMFD guide
│ ├── PRNU.md # PRNU guide
│ ├── Frequency.md # FFT/DCT guide
│ ├── Deepfake.md # Deepfake guide
│ └── Resampling.md # Resampling guide
│
├── docs/ # Documentation
│ ├── API.md # API documentation
│ ├── DEPLOYMENT.md # Deployment guide
│ ├── PROJECT_SUMMARY.md # Project overview
│ └── TECHNIQUES.md # Techniques reference
│
├── tests/ # Unit tests
│ ├── __init__.py
│ ├── test_ela.py # ELA tests
│ ├── test_metadata.py # Metadata tests
│ └── test_integration.py # Integration tests
│
├── assets/ # Static files
│ ├── style.css # Custom CSS
│ └── sample images/ # Sample test images
│ └── sampleImg.jpeg # Default sample image
│
├── .streamlit/ # Streamlit config
│ └── config.toml # Theme & server settings
│
└── temp/ # Temporary processing files
└── .gitkeep

🔬 Usage Guide

Basic Workflow

1. View Sample Image: App loads with default sample image automatically
2. Upload Your Image (Optional): Click "Choose an Image" in sidebar to analyze your own
3. Learn About Techniques: Click technique description buttons in sidebar for guidance
4. Select Analysis Tab: Navigate to the technique you want to use
5. Configure Parameters: Adjust sliders/options as needed
6. Run Analysis: Click the analysis button
7. Review Results: View visualizations, authenticity scores, and interpretations

Technique Descriptions (NEW)

Access built-in educational guides via sidebar buttons:

• 📚 Technique Descriptions Section - Click any technique to learn
• Non-technical explanations - Understand what each tool does
• Interpretation guides - Learn to read results (normal vs. suspicious)
• Real-world examples - See practical use cases
• Limitations explained - Understand reliability and caveats

Analysis Techniques Explained

Error Level Analysis (ELA)

Detects compression artifacts by comparing the original image with a recompressed version. Manipulated regions show different error levels.

Use Case: Quick initial screening for tampering

Metadata Forensics

Examines EXIF data, timestamps, GPS coordinates, and software signatures for inconsistencies.

Use Case: Verify image authenticity and origin

Histogram Analysis

Analyzes color distribution patterns. Manipulated regions often show statistical anomalies.

Use Case: Detect color/brightness adjustments

Noise Inconsistency

Uses high-pass filtering to detect regions with different noise characteristics.

Use Case: Identify spliced or cloned regions

JPEG Ghost Detection

Performs multiple recompressions to detect prior editing cycles.

Use Case: Determine editing history

Copy-Move Forgery Detection (CMFD)

Identifies duplicated regions within the same image.

Use Case: Detect cloning tools usage

PRNU Analysis

Extracts sensor-specific noise patterns unique to each camera.

Use Case: Verify camera source consistency

Frequency Domain Analysis

Analyzes FFT/DCT coefficients for manipulation artifacts.

Use Case: Detect advanced editing techniques

Deepfake Detection

Identifies GAN-generated or AI-manipulated faces.

Use Case: Detect synthetic or deepfake images

Resampling Detection

Identifies traces of image resizing or interpolation.

Use Case: Detect resolution manipulation

⚙️ Configuration

Theme Customization

Edit .streamlit/config.toml:

[theme]
base="dark"primaryColor="#00ff41"# Neon green accentbackgroundColor="#0e1117"secondaryBackgroundColor="#262730"textColor="#fafafa"

🌐 Deployment

Deploy to Streamlit Cloud

1. Push code to GitHub
2. Go to share.streamlit.io
3. Connect your GitHub repository
4. Select app.py as the main file
5. Click "Deploy"

📊 Performance

• ELA: ~2-5 seconds per image
• Metadata: <1 second
• Histogram: ~1-2 seconds
• CMFD: ~10-30 seconds (depending on image size)
• Deepfake: ~5-10 seconds
• PRNU: ~15-30 seconds

Benchmarked on Intel i5, 8GB RAM, 1920x1080 images

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

Acknowledgments

• Streamlit - For the excellent web framework
• PIL/Pillow - Image processing library
• OpenCV - Computer vision algorithms
• SciPy - Scientific computing tools

📚 References

1. Farid, H. (2009). "Image Forgery Detection"
2. Fridrich, J. (2009). "Digital Image Forensics"
3. Bayar, B. & Stamm, M. (2018). "Constrained Convolutional Neural Networks"

📧 Contact

• Author: CodeRafay
• GitHub: @CodeRafay
• Repository: Forensic-Image-Analysis-Toolkit

🔮 Roadmap

• Batch processing for multiple images
• PDF report generation
• Machine learning-based forgery classifier
• Video frame analysis
• REST API for integration

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⭐ If you find this project useful, please star it on GitHub!