reverse image search · content matching · dedup
Perceptual hashes produce similar hashes for similar content — unlike cryptographic hashes where one bit difference changes everything. aHash (average), dHash (difference), pHash (DCT-based). Type text for a simulation, or load real images to compute genuine aHash/dHash fingerprints.
Text or Image A box — or click Load Image A / drop an image to compute a real perceptual hash.Text or Image B (compare) the same way.aHash and dHash for both inputs, plus their Hamming distance and a similarity percentage.Hamming distance to judge how alike the two inputs are — 0 means identical, higher values mean more difference (out of 64 for images, 16/15 for text).Clear to reset both boxes and start over.See aHash and dHash produce fingerprints in real time without setting up an image library — the same idea powers production pHash implementations.
Reverse image search, duplicate detection, and content matching all rely on "similar content → similar hash." Watch that principle work on text first.
The Hamming distance gives a concrete score — out of 16 for aHash and 15 for dHash — plus a percentage, so "close" is measurable, not a guess.
Unlike SHA-256, flipping one bit doesn't avalanche here. Similar inputs stay near each other in hash space — a core property of perceptual hashing.
Everything runs in your browser. Nothing is uploaded, logged, or sent to a server.
A perceptual hash is a fingerprint designed so that similar content produces similar hashes. Unlike cryptographic hashes, a tiny change in input doesn't scramble the result. Common variants are aHash (average), dHash (difference), and pHash (DCT-based).
Yes — when you load an image, this tool computes genuine aHash and dHash by resizing to a fixed grid (8×8 for aHash, 9×8 for dHash), converting to grayscale, and thresholding to a 64-bit fingerprint. Text input remains a simulation.
aHash divides content into segments and marks each one 1 or 0 by comparing it to the average. dHash instead compares each segment to its neighbor, which makes it more robust to brightness shifts in real images.
It counts the positions where two hashes differ. A distance of 0 means identical, while higher distances mean less similarity. This tool also shows it as a percentage.
The text is split into 16 segments. aHash produces one bit per segment (16 total), while dHash compares consecutive segments, yielding 15 differences. For images, both produce a full 64-bit hash.
Yes. Click Load Image A / Load Image B (or drag & drop an image onto either panel) to compute a real 64-bit aHash and dHash. Load two images to measure their similarity.
Never. All hashing runs locally in JavaScript. Your input is not sent to, stored on, or logged by any server.