Image Hash Generator
Generate perceptual image hashes (aHash, dHash, pHash) and compare them with Hamming distance. Detect duplicate and similar images — 100% client-side.
Image Hash Generator
Image Hash Generator creates perceptual hashes (aHash, dHash, pHash) for images. Hashes can be used to detect duplicates and similar images.
How to Use Image Hash Generator
- Upload an image by dragging and dropping or clicking to browse.
- Click Generate Hashes to compute all three perceptual hash values.
- Copy any hash with the copy button. Optionally paste another hash to compare similarity.
Frequently Asked Questions
Got questions about image hashing?
What are perceptual image hashes?
Perceptual hashes create a fingerprint of an image based on its visual content. Similar images have similar hashes, making them ideal for duplicate detection.
What is Hamming distance?
Hamming distance measures how many bits differ between two hashes. A lower distance means images are more similar. For a 64-bit hash, distance under 10 indicates high similarity.
Are my images uploaded to a server?
No, all hash computation runs entirely in your browser via Web Workers. Your images never leave your device.
Automated E2E Test Suite Logs
Our build systems validate edge-case processing under extreme conditions before each deployment.
Tool Changelog & Updates
- Feature: Batch hash computation — upload multiple images and compute aHash, dHash, and pHash for all at once.
- UX: Card-based UI with thumbnails and all three hashes displayed per image — click any hash row to copy.
- Feature: Added comprehensive Vitest test suite covering Tool rendering, worker operations, and shared operation logic — all 871 image tests passing.
- Fix: Fixed OffscreenCanvas mock patterns in tests for proper constructor instantiation.
- Feature: Initial release with aHash, dHash, and pHash perceptual image hash generation — 100% client-side via Web Worker.
- Feature: Hash comparison with Hamming distance and match percentage calculation.
- UX: One-click copy buttons for each hash value with visual feedback.