Image Fingerprint & Compare
Reduce a photo to a short perceptual fingerprint, a hash that stays the same when the image is resized, recompressed, or renamed. Drop one image to get its fingerprint, or two to compare them. A close match is a strong lead that the pictures share a source, which is how you spot a reused or stolen profile photo. Everything runs in your browser. Your images are never uploaded, sent anywhere, or stored.
What a perceptual hash is, and how to read a match
Not the same as a file hash
A normal checksum like MD5 or SHA-256 changes completely if a single byte changes, so re-saving a photo at a different quality, resizing it, or stripping its metadata produces an entirely different hash. That is perfect for verifying a file is bit-for-bit identical, but useless for asking "is this the same picture?" A perceptual hash answers the second question. It looks at the image content, not the bytes, and produces a fingerprint that stays close even after the image is resized, recompressed, or lightly edited.
How it works
This tool computes two well-known perceptual hashes. The average hash (aHash) shrinks the image to 8 by 8 grayscale pixels and records, for each, whether it is brighter or darker than the average. The difference hash (dHash) shrinks it to 9 by 8 and records, across each row, whether each pixel is brighter than the one to its right. Each produces 64 bits, shown as 16 hex characters. Because both throw away color, fine detail, and exact size, two versions of the same photo land on nearly the same fingerprint.
Reading the distance
To compare two fingerprints you count how many of the 64 bits differ, the Hamming distance. A distance of 0 means the fingerprints are identical. A small distance (a handful of bits) means the images are almost certainly the same picture, just resized or recompressed. A large distance means they are different images. The difference hash is usually the more reliable of the two, so this tool leads with it and shows the average hash as a cross-check.
Why it matters for vetting
Fraudulent identities, including the kind used in remote-hire and romance scams, often reuse a single stolen or AI-generated headshot across many fake profiles, or lift a stock photo. If a candidate's photo fingerprints close to an image you found elsewhere, that is a strong lead worth running down. The fingerprint is also short and shareable, so two people can compare hashes without exchanging the actual image.
What this does and does not prove
A close match is a strong signal that two images share a source, not proof that two accounts are the same person: anyone can copy a public photo. Perceptual hashes can occasionally collide on genuinely different images, and they can be defeated on purpose by heavy editing, a tight crop, a flip, or a rotation, none of which this simple version is invariant to. Treat a match as a lead to corroborate, and treat a non-match as "not via this method," not as proof the images are unrelated. Everything is computed locally in your browser; no image leaves your device.