prcp

A tiny and fast perceptual image hashing algorithm for JavaScript.

• Small. 837 bytes (minified and gzipped). No dependencies.
• Portable. Supports modern browsers and Node.js.
• Fast.

This algorithm is based on the paper Block Mean Value Based Image Perceptual Hashing.

Usage

NodeJS

1. Install using npm

npm install prcp

2. Create a hash

const prcp = require('prcp')

const hash = prcp(image) // C3257FEC3610E8F1

Browser

1. Import using unpkg

<script src="https://unpkg.com/prcp/dist/index.browser.js"></script>

2. Create a hash

<script>
    const hash = prcp(image) // 3610E8F1C3257FEC
</script>

API

prcp(image, precision)

Creates a perceptual hash based on the given image.

| parameter | type | required | default | description | | :--- | :--- | :---: | :---: | :--- | | image | ImageData | ✓ | | Image to hash | | precision | Number | | 8 | Hash size (N^2 bits) |

Hashes can be compared using the Levenshtein or Hamming distance.

Similar images will not always have a distance equal to 0, which means that you will have to decide at what distance you will evaluate images as equal.

Use cases

A perceptual hashing algorithm could be used to:

• Avoid storing the same image multiple times
• Identify plagiarism

Database lookup

If you need to search a database, I recommend the following approach:

1. Create a table with four columns: each column will contain an 16 bits slice of the 64 bits hashes. The number of columns, and their size is up to you.

CREATE TABLE images (
    H CHAR(16), -- Full hash
    H0 CHAR(4), -- Hash parts
    H1 CHAR(4),
    H2 CHAR(4),
    H3 CHAR(4)
);

2. Split your query hash in four parts.

const parts = ['3610', 'E8F1', 'C325', '7FEC']

3. Query this table. In this case, it gives you every hash that are within 3 bits (4 - 1) of the query hash.

SELECT H FROM images WHERE H0 = parts[0] OR H1 = parts[1] OR H2 = parts[2] OR H3 = parts[3]

4. Compute the exact Hamming distance for each returned hash with your query hash.

const query = '3610E8F1C3257FEC'                  // Query hash
const hashes = ['36107FEC3610E8F1', '...', '...'] // Database hashes

for (const hash of hashes) {
  const distance = hamming(query, hash)
  
  // ...
}

The preceding code is for demonstration purposes only and should be tweaked to fit your use case.

Example

Original

Watermark

Original hash: ffff23c1c0e0e060
Watermark hash: ffff23c1c0e0e060
Hamming distance: 0

Different

Original hash: ffff23c1c0e0e060
Different hash: 3f0020783d3f1f1f
Hamming distance: 14