Perceptual hashing is the use of an algorithm that attempts to fingerprint multimedia for identification and comparison. Perceptual hashes of two similar multimedia should be similar.
Trust & Safety tools for working together to fight digital harms.
pHash - the open source perceptual hash library
Identifying and removing near-duplicate images using perceptual hashing.
Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates
Fast Near-Duplicate Image Search and Delete using pHash, t-SNE and KDTree.
A catalog of naturally occurring images whose Apple NeuralHash is identical.
vips-powered ruby gem to measure images similarity, implementing dHash and IDHash algorithms
Tool to detect (and get rid of) similar images using perceptual hashing (pHash lib)
Golang library for computing perceptual hashes of images
sharp based perceptual hash implementation
Lightroom plug-in to deduplicate images based on perceptual hash algorithms
A simple perceptual hash library in pure Go.
Calculate PhotoDNA hashes using Python
Dart package for comparing images. Find the difference between two images by using a variety of image comparison techniques.
[FAccT 2022] Source code for our paper "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash".
CLI Java wrapper for the PhotoDNA library
Reverse image search utility based on perceptual hash algorithms
Image comparison by hash codes
open source perceptual hashing library
C/C++ replacement for the 'imagehash' python package