🔍 An Open Source Digital Image Forensics Toolkit

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📋 Table of Contents

• 🎯 Introduction
• 🚀 Quick Start
• ⚡ FeaturesLaunch-LOOK-DGC.bat
• 📸 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

# Run with Docker Compose
docker-compose up

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

🛠️ 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

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📸 Screenshots

📋 General Tools: Original Image, File Digest, Hex Editor, Similar Search

🔬 Visual Inspection: Magnifier, Histogram, Reference Comparison

🎯 Detail Analysis: Gradient, Edge Filter, Wavelet, Frequency Split

🎨 Color Analysis: RGB/HSV Plots, Space Conversion, PCA, Statistics

📡 Noise Analysis: Noise Separation, Min/Max Deviation, Bit Planes

📷 JPEG Analysis: Quality Estimation, Error Level Analysis

⚠️ Tampering Detection: Copy-Move, Splicing, Resampling, Filtering

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💻 Installation

📋 Prerequisites

• 🐍 Python 3.11+
• 💾 4GB+ RAM (recommended)
• 🖥️ Windows/Linux/macOS

🚀 Method 1: Quick Start (Recommended)

Windows

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

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

🚀 Quick Docker Start

Windows

# Install VcXsrv X Server first
# Download: https://sourceforge.net/projects/vcxsrv/

# Run LOOK-DGC
docker-compose -f docker-compose.windows.yml up

Linux

# Allow X11 forwarding
xhost +local:docker

# Run LOOK-DGC
docker-compose up

macOS

# Install XQuartz first
# Download: https://www.xquartz.org/

# Run LOOK-DGC
docker-compose up

🔧 Manual Docker Commands

Build Image

docker build -t look-dgc:latest .

Run Container

# Windows
docker run -it --rm -e DISPLAY=host.docker.internal:0 look-dgc

# Linux
docker run -it --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix look-dgc

# macOS
docker run -it --rm -e DISPLAY=host.docker.internal:0 look-dgc

📁 Volume Mounts

• ./images:/app/images - Input images
• ./output:/app/output - Analysis results

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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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🤝 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! 🕵️♂️