Build a solution to detect duplicate pirated images(using MERN stack)

[anoop2000]

This project implements an advanced image duplicate detection system that helps identify pirated or duplicate images by comparing them against original content. The system is designed to be robust, efficient, and capable of detecting various types of image manipulations and duplications.
Implemented Techniques

1. Perceptual Hashing (pHash)
   Implements a perceptual hashing algorithm that generates unique fingerprints for images
   Converts images to grayscale and resizes them to a standard size
   Applies DCT (Discrete Cosine Transform) to create a compact representation
   Compares hash values to detect similar images even after minor modifications
   Effective for detecting resized, slightly modified, or watermarked images
2. Hamming Distance Comparison
   Calculates the Hamming distance between image hashes
   Provides a numerical measure of similarity between images
   Threshold-based detection system (below 50 indicates high similarity)
   Robust against minor image alterations and compression artifacts
   Helps identify images that have been slightly modified or cropped
3. Structural Similarity Index (SSIM)
   Implements SSIM algorithm for structural similarity comparison
   Analyzes luminance, contrast, and structure of images
   Provides similarity scores between 0 and 1
   Threshold of 0.6 for determining similarity
   Effective for detecting images with similar content but different formats
   Image Detection Capabilities
   The system can effectively detect:
   Resized images
   Cropped versions of original images
   Images with added watermarks
   Slightly modified images (brightness, contrast adjustments)
   Compressed versions of original images
   Images with minor color alterations
   Blurred or sharpened versions
   Images with added text or overlays
   Technical Implementation
   Frontend Technologies
   React.js for the user interface
   Modern CSS with responsive design
   Lucide React for icons
   File handling and preview capabilities
   Real-time status updates and error handling
   Backend Technologies
   Node.js with Express.js server
   Sharp for image processing
   Efficient image resizing
   Format conversion
   Image analysis
   Multer for file upload handling
   Secure file uploads
   File type validation
   Size restrictions
   Perceptual hashing libraries
   Image hash generation
   Hash comparison algorithms
   Error handling and logging
   Key Features
   Multiple image upload support
   Real-time comparison results
   Detailed similarity metrics
   Visual feedback for pirated/original content
   Responsive design for all devices
   Secure file handling
   Efficient image processing
   Clear status indicators
   Performance Considerations
   Optimized image processing
   Efficient hash generation
   Quick comparison algorithms
   Memory-efficient file handling
   Scalable architecture
   This project provides a comprehensive solution for image duplicate detection, combining multiple techniques to ensure accurate results while maintaining good performance and user experience. The system is particularly useful for content creators, digital asset managers, and platforms that need to protect their image content from unauthorized use.