Developer tool / 100% free

Image Fingerprint

Generate perceptual hashes for an image: average hash, difference hash and colour statistics. Unlike a checksum, these stay similar when an image is resized or recompressed, which is what makes them useful for finding duplicates. It is free, needs no account, and adds no watermark to your result.

Built for: image perceptual hash

Image Fingerprint

Add your files. Recommended settings are ready, so you can process in one click.

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PDF, Word, images, audio, video, archives and more

Up to 100 files · 250 MB · No account
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  3. 3Download or keep going
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When you need this

What people use it for.

Finding near-duplicate photos in a large library
Detecting whether an image has been reused or reposted elsewhere
Deduplicating an asset collection where files were resaved at different sizes
How it works

What happens to your file.

Average and difference hashes are computed from a downscaled greyscale version of the image. Because those two ignore colour entirely, a separate 48-bit colour hash records which channel outweighs which across a 4×4 grid, and comes back with the average colour, brightness and saturation. Ordering the channels against each other keeps that hash about hue, so a darker copy of the same picture still matches. All of it is returned as JSON, and similar images produce similar hashes.

Where it stops: These are similarity measures, not identity proofs. Heavy cropping changes composition enough to change the hashes substantially.

What this tool includes

Everything you need, without a paywall.

Free Pro batch queue included
Average hash
Difference hash
Colour hash the grey hashes miss
JSON output
Questions

Common questions.

How is this different from an MD5 checksum?

A checksum changes completely if a single byte changes, so a resaved image looks entirely unrelated. A perceptual hash stays close when the picture still looks the same.

How do I compare two hashes?

Count the differing bits, known as the Hamming distance. A small distance means the images are visually similar. The colour hash compares the same way.

Will it match a cropped image?

Often not. Cropping changes composition significantly, which is exactly what these hashes measure.

Can it tell apart two images that differ only in colour?

Yes, and that is what the colour hash is for. The average and difference hashes are computed on a greyscale copy, so a red and a blue version of the same picture score as identical there; the colour hash and the average colour are what separate them.

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