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Image Hash Comparator

Hash size

Larger hashes are more sensitive but slower to calculate.

Algorithms

After changing algorithms or hash size, click Recalculate hashes.

Compare hashes

Compare the perceptual hashes for Image A and Image B using Hamming distance.

Add one image to each slot to compare the results.

Compare two JPEG, PNG, or WebP images by calculating perceptual hashes and measuring the Hamming distance between them. Unlike a normal file checksum, this tool checks if two images look similar, even if they have been resized, compressed, or lightly edited.

Hash types and sensitivity

Choose between aHash, dHash, pHash, and wHash to compare different visual properties. aHash compares average brightness, dHash detects local pixel differences, and pHash and wHash focus on broader visual structure. Using multiple hashes provides a more balanced comparison result.

The hash size controls the level of detail in the fingerprint. An 8×8 hash is faster and good for catching obvious duplicates, while a 16×16 hash creates a 256-bit fingerprint that is much more sensitive to minor changes.

Hamming distance verdicts

The Hamming distance counts how many bits are different between the two hashes. A distance of zero means an exact match for that specific algorithm.

A lower distance means the hashes are closer, often suggesting the images are just resized or compressed versions of the same file. A higher distance indicates the hashes are further apart and the visual differences are more significant.

Exporting the comparison

After comparing both images, the table shows the distance, similarity percentage, and a plain-language verdict. You can copy the comparison summary directly to your clipboard or download it as a CSV.

The export includes the selected algorithms, both filenames, and the total hash bits. This is useful for auditing website images, keeping QA notes, or documenting duplicate assets.

Frequently Asked Questions

It compares two images by calculating perceptual hashes for each one, then measuring the Hamming distance between the hashes. This helps you check whether two images are visually identical, very similar, somewhat related, or different.

The tool supports JPEG, PNG, and WebP images. These are the most common web image formats for screenshots, product images, previews, thumbnails, and optimized site assets.

No.

Hamming distance counts how many hash bits are different between two image hashes. A lower distance usually means the images are more visually similar. A distance of 0 means the selected hash produced an exact match.

aHash compares average brightness, dHash compares local pixel differences, pHash compares low-frequency visual structure, and wHash uses a wavelet-based view of the image. Each method sees similarity slightly differently.

There is no single perfect hash for every image. pHash and wHash are often better for broader visual similarity, while aHash and dHash are useful for quick duplicate or near-duplicate checks. Compare multiple hashes for a more balanced result.

8×8 creates a 64-bit hash and is faster and less sensitive. 16×16 creates a 256-bit hash and is more sensitive to smaller differences, but can take longer to calculate.

No. Perceptual hashes compare visual similarity, not file identity. Two different files can look the same and produce a close perceptual hash. For exact file identity, use a cryptographic hash such as SHA-256.

Often, yes. Perceptual hashes are designed to stay similar when an image is resized, compressed, or lightly edited. Strong crops, heavy filters, added text, or major layout changes can increase the distance.

Yes. You can download a CSV that includes the selected hash algorithms, both image filenames, both hash values, Hamming distance, total hash bits, similarity percentage, and verdict.

Yes. The tool includes a copy button for the comparison summary so you can paste the result into notes, QA reports, tickets, documentation, or a spreadsheet.

Yes.

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