dhash

A dhash implementation for Deno.

A fast algorithm that allows checking if two images are "kind of" the same (the same source image, slightly modified). Examples:

• A resized, compressed, slightly cropped, or color-altered image compared with the original
• A watermarked image versus it's source
• Meme images (mostly the same template, different text)

It does this by computing a perceptual hash of each image and then using it to compare similarity.

Perceptual hashing is the use of a fingerprinting algorithm that produces a 
snippet or fingerprint of various forms of multimedia.

Based on the "Kind of Like That" article by Dr. Neal Krawetz.

Demo

Live demo (Deno Deploy): https://dhash.claudiuceia.deno.net/

The demo source lives in docs/ (docs/main.tsx, docs/client.js) and is deployed separately from the JSR package.

Usage

You can compare dhash values by simply computing the Hamming distance between them:

• A distance of 0 represents an identical, or very similar image
• A distance greater than 10 means that you're most likely dealing with a different image
• A distance between 1 and 10 may indicate that you're dealing with variations of the same base image

const [hash1, hash2] = await Promise.all([
  dhash("./tests/dalle.png"),
  dhash("./tests/dalle-copyright.png"),
]);

console.log(compare(hash1, hash2));

Bit convention: this implementation sets bit 1 when the pixel intensity increases left-to-right (left < right). Use dhash(src, { invert: true }) if you need the opposite convention to match another implementation.

API

In addition to dhash() and compare():

toAscii(hash: string, chars?: [string, string]): string
raw(hash: string): Promise<Uint8Array> // PNG bytes for the 8x8 fingerprint
save(hash: string, filePath: string): Promise<void> // writes `${filePath}.png`

toAscii() always renders an 8x8 matrix (64 bits); leading zero bits are preserved.

Development

• Run tests: deno task test (requires --allow-read --allow-ffi --allow-env because sharp uses native bindings and reads fixtures).

License

MIT © Claudiu Ceia