🔍 An Open Source Digital Image Forensics Toolkit

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📋 Table of Contents

• 🎯 Introduction
• 🚀 Quick Start
• ⚡ Features
• 📸 Screenshots
• 💻 Installation
• 🐳 Docker Setup
• 📚 Documentation
• 🤝 Contributing

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🎯 Introduction

"Forensic Image Analysis is the application of image science and domain expertise to interpret the content of an image and/or the image itself in legal matters." - Scientific Working Group on Imaging Technologies

LOOK-DGC is a comprehensive digital image forensics toolkit developed by Gopichand. It provides a fully integrated environment for analyzing digital images to detect tampering, forgery, and manipulation.

🌟 Why LOOK-DGC?

• 🔓 Open Source: Free and accessible to everyone
• 🎓 Educational: Learn digital forensics techniques
• 🔬 Research-Ready: Implement latest algorithms from research papers
• 🛡️ Professional: Used by investigators and security professionals
• 🌍 Community-Driven: Democratizing image forensics tools worldwide

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🚀 Quick Start

Option 1: Docker (Recommended)

# Clone the repository
git clone https://github.com/Gooichand/LOOK-DGC.git
cd LOOK-DGC

# For platform-specific Docker setup instructions, see DOCKER_README.md

Option 2: Direct Installation

# Clone and setup
git clone https://github.com/Gooichand/LOOK-DGC.git
cd LOOK-DGC

# Windows
Launch-Look-DGC.bat

# Linux/macOS
./launch-look-dgc.sh

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⚡ Features

🖥️ Modern Interface

• 🎨 Qt-based GUI: Professional multi-window management
• 📁 Format Support: JPEG, PNG, TIFF, BMP, WebP, PGM, PFM, GIF
• ⚡ Responsive Viewer: Real-time pan, zoom, and navigation
• 🔄 Interactive Analysis: Live algorithm parameter adjustment
• 📊 Export Capabilities: Visual and textual result export
• ❓ Built-in Help: Comprehensive tutorials and explanations

🤖 AI Solutions (TruFor)

LOOK-DGC includes an AI Solutions tool group featuring TruFor, an AI-based image forensic tool.

TruFor is designed to:

• Detect image manipulations using deep learning methods
• Complement traditional forensic tools with data-driven analysis
• Provide probabilistic insights rather than deterministic decisions

⚠️ Note: AI-based results may have limitations depending on image quality, dataset bias, and model assumptions. TruFor outputs should be interpreted alongside traditional forensic analyses.

🛠️ Analysis Tools

📋 General Tools

• 📷 Original Image: Unaltered reference display
• 🔐 File Digest: Cryptographic hashes and file information
• ⚙️ Hex Editor: Raw byte-level file examination
• 🔍 Similar Search: Online reverse image search integration

📊 Metadata Tools

• 🏗️ Header Structure: Interactive file structure visualization
• 📋 EXIF Full Dump: Complete metadata extraction and analysis
• 🖼️ Thumbnail Analysis: Embedded thumbnail comparison
• 🌍 Geolocation Data: GPS coordinate mapping and visualization

🔬 Inspection Tools

• 🔍 Enhancing Magnifier: Forgery-detection enhanced magnification
• 📈 Channel Histogram: RGB/composite histogram analysis
• ⚖️ Global Adjustments: Brightness, hue, saturation manipulation
• ↔️ Reference Comparison: Synchronized dual-image comparison

🎨 Color Analysis

• 📊 RGB/HSV Plots: Interactive 2D/3D color space visualization
• 🔄 Space Conversion: Multiple color space transformations
• 🧮 PCA Projection: Principal component analysis projection
• 📏 Pixel Statistics: Comprehensive per-pixel statistical analysis

📡 Noise Analysis

• 🔊 Noise Separation: Multi-type noise component extraction
• 📊 Min/Max Deviation: Block-based statistical deviation analysis
• 🔢 Bit Plane Analysis: Individual bit layer examination
• 🆔 PRNU Identification: Camera sensor pattern noise analysis

📷 JPEG Analysis

• 📊 Quality Estimation: Quantization table analysis
• ⚡ Error Level Analysis: Compression level difference visualization
• 🔄 Multiple Compression: Machine learning compression detection
• 👻 JPEG Ghost Maps: Compression artifact trace visualization

⚠️ Tampering Detection

• 📋 Copy-Move Forgery: Feature descriptor cloning detection
• ✂️ Composite Splicing: DCT statistical splicing detection
• 🔄 Image Resampling: 2D interpolation trace detection
• 🎛️ Contrast Enhancement: Color distribution manipulation analysis

🔧 Additional Tools

• 🔍 Median Filtering: Nonlinear filtering trace detection
• 💡 Illuminant Mapping: 3D surface light direction estimation
• 🔴 Dead/Hot Pixels: Sensor defect detection and correction
• 👁️ Stereogram Decoder: Hidden 3D image extraction

📄 PDF Report Generation

LOOK-DGC supports generating detailed PDF reports from analysis tools that provide structured data. The following tools include PDF export capabilities:

• 📷 Original Image: Baseline image display with metadata summary
• 📊 Histogram Analysis: Statistical data, channel information, and histogram plot visualization
• 🏗️ Header Structure: File header analysis summary and metadata overview
• 🔊 Noise Analysis: Noise estimation parameters, processing settings, and processed image

To generate a PDF report, select the desired analysis tools and use the "Generate Report" option from the File menu.

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📁 Project Structure

Below is an overview of the main folders and files in the LOOK-DGC repository to help new contributors understand the codebase easily:

LOOK-DGC/
├── gui/                  # Main GUI application source code
│   ├── modules/          # Core forensic analysis modules
│   └── ui/               # Qt UI layouts and components
├── images/               # Sample input images
├── output/               # Generated analysis results
├── demo-ss/              # Screenshots for README
├── docker/               # Docker configuration files
├── Launch-LOOK-DGC.bat   # Windows launcher
├── launch-look-dgc.sh    # Linux/macOS launcher
├── docker-compose.yml    # Docker Compose config
├── Dockerfile            # Docker build file
├── LICENSE               # MIT License
├── README.md             # Project documentation
└── HOW_IT_WORKS.md       # Forensic workflow explanation


## 📸 Screenshots

<p align="center">
  <img src="Demo-Ss/0_gen.png" alt="General Tools" width="800"/>
  <br><b>📋 General Tools</b>: Original Image, File Digest, Hex Editor, Similar Search
</p>


<p align="center">
  <img src="Demo-Ss/2_ins.png" alt="Visual Inspection" width="800"/>
  <br><b>🔬 Visual Inspection</b>: Magnifier, Histogram, Reference Comparison
</p>

<p align="center">
  <img src="Demo-Ss/3_detai.png" alt="Detail Analysis" width="800"/>
  <br><b>🎯 Detail Analysis</b>: Gradient, Edge Filter, Wavelet, Frequency Split
</p>

<p align="center">
  <img src="Demo-Ss/4_clour.png" alt="Color Analysis" width="800"/>
  <br><b>🎨 Color Analysis</b>: RGB/HSV Plots, Space Conversion, PCA, Statistics
</p>

<p align="center">
  <img src="Demo-Ss/5_noise.png" alt="Noise Analysis" width="800"/>
  <br><b>📡 Noise Analysis</b>: Noise Separation, Min/Max Deviation, Bit Planes
</p>

<p align="center">
  <img src="Demo-Ss/6_jpeg.png" alt="JPEG Analysis" width="800"/>
  <br><b>📷 JPEG Analysis</b>: Quality Estimation, Error Level Analysis
</p>

<p align="center">
  <img src="Demo-Ss/7_tampe.png" alt="Tampering Detection" width="800"/>
  <br><b>⚠️ Tampering Detection</b>: Copy-Move, Splicing, Resampling, Filtering
</p>

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## 💻 Installation

### 📋 Prerequisites
- **🐍 Python 3.11+** 
- **💾 4GB+ RAM** (recommended)
- **🖥️ Windows/Linux/macOS**

### 🚀 Method 1: Quick Start (Recommended)

#### Windows
```cmd
git clone https://github.com/Gooichand/LOOK-DGC.git
cd LOOK-DGC
Launch-Look-DGC.bat

Linux/macOS

git clone https://github.com/Gooichand/LOOK-DGC.git
cd LOOK-DGC
chmod +x launch-look-dgc.sh
./launch-look-dgc.sh

🔧 Method 2: Manual Setup

1️⃣ Clone Repository

git clone https://github.com/Gooichand/LOOK-DGC.git
cd LOOK-DGC

2️⃣ Create Virtual Environment

# Using venv
python -m venv .venv

# Activate (Windows)
.venv\Scripts\activate.bat

# Activate (Linux/macOS)
source .venv/bin/activate

3️⃣ Install Dependencies

cd gui
pip install -r requirements.txt

Alternatively, you can use the automated dependency checker:

python check_deps.py

Or validate dependencies before launching:

python validate_deps.py

4️⃣ Launch Application

python look-dgc.py

🐧 Linux Additional Setup

If you encounter Qt platform plugin errors:

sudo apt install -y libxcb-cursor-dev

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🐳 Docker Setup

� Docker Setup (See Detailed Guide)

For Linux, Windows, and macOS, please refer to DOCKER_README.md for platform-specific Docker instructions.

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📚 Documentation

📖 Additional Resources

• 🔍 How It Works - Complete analysis guide and workflows
• 🐳 Docker Setup Guide - Container deployment instructions
• 📄 License - MIT License terms and conditions

🎓 Learning Resources

• 📚 Research Papers: Implementation references in bibliography
• 🎯 Practical Examples: Try analyzing known edited vs. original images
• 🔬 Algorithm Details: Source code documentation and comments
• 🌐 Community: Join discussions and contribute improvements

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🧪 Testing

LOOK-DGC includes automated tests to ensure code quality and functionality.

Running Tests

Install Test Dependencies

# Navigate to tests directory
cd tests

# Install testing requirements
pip install -r test_requirements.txt

Run Unit Tests

# Run all unit tests
python -m pytest unit/ -v

# Run with coverage report
python -m pytest unit/ -v --cov=../gui --cov-report=html

# Run specific test file
python -m pytest unit/test_utility.py -v

Run Integration Tests

# Run integration tests
python -m pytest integration/ -v

Run All Tests

# Run complete test suite
python -m pytest -v

Test Coverage

Test coverage reports are generated in the tests/htmlcov/ directory when running with --cov-report=html.

CI/CD

GitHub Actions workflows automatically run tests on:

• Every push to main and develop branches
• Every pull request
• Multiple operating systems (Ubuntu, Windows)
• Multiple Python versions (3.11, 3.12)

View test results and coverage on GitHub Actions.

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🤝 Contributing

We welcome contributions from the community! Here's how you can help:

🐛 Bug Reports

• Use GitHub Issues to report bugs
• Include system information and steps to reproduce
• Attach sample images if relevant (ensure no sensitive data)

💡 Feature Requests

• Suggest new analysis algorithms
• Propose UI/UX improvements
• Request additional file format support

🔧 Code Contributions

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Submit a pull request

📖 Documentation

• Improve existing documentation
• Add tutorials and examples
• Translate to other languages

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🌟 Acknowledgments

🔬 Research Foundation

This project implements algorithms from cutting-edge research papers in digital image forensics:

• Image Resampling: "Exposing Digital Forgeries by Detecting Traces of Re-sampling" (Popescu & Farid)
• JPEG Ghosts: "Exposing Digital Forgeries from JPEG Ghosts" (H. Farid)
• Noise Analysis: "Using noise inconsistencies for blind image forensics" (Mahdian & Saic)
• Noiseprint: "A CNN-based camera model fingerprint" (Cozzolino & Verdoliva)

🛠️ Technology Stack

• 🐍 Python: Core programming language
• 👁️ OpenCV: Computer vision and image processing
• 🧮 NumPy/SciPy: Mathematical computations and algorithms
• 🤖 TensorFlow: Machine learning and AI-powered detection
• 🖥️ PySide6: Modern Qt-based user interface
• 🐳 Docker: Containerization and easy deployment

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🔍 LOOK-DGC - Making Digital Image Forensics Accessible to Everyone
Developed with ❤️ by Gopichand

⭐ Star this project • 🐛 Report Bug • 💡 Request Feature

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Ready to become a digital detective? Clone the repository and start your first investigation! 🕵️♂️