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Image Similarity Checker — Perceptual Hash (pHash, dHash, aHash) & Duplicate Detector
Fingerprint any image with perceptual hashes and compare two images to see how similar they are — detect duplicates, resized copies, or re-compressed versions. Computes aHash, dHash, and pHash with a Hamming-distance similarity score, entirely in your browser. Nothing is uploaded.
Upload image A to fingerprint it, then add image B to measure how similar they are. Read locally, never uploaded.
Fingerprint an image with perceptual hashes and compare two images to see how similar they are — detecting duplicates, resized copies, and re-compressed versions that a byte-for-byte check would miss. It computes aHash, dHash, and pHash and reports a Hamming-distance similarity score, entirely in your browser with nothing uploaded.
How It Works
Choose a tool
Pick from 120+ tools to resize, convert, compress, or enhance your image.
Upload & edit
Drag and drop your image and adjust the settings. It stays on your device.
Download
Save your result instantly — no watermark, no sign-up required.
Why Image Machine?
Your files never leave your device
All processing runs locally in your browser. Your images are never uploaded to a server.
Completely free
Every tool is free, with no limits, no watermarks, and no hidden costs.
Lightning fast
No upload waiting — your images are processed instantly on your own device.
Professional quality
Pixel-perfect output with full control over format, size, and quality.
Frequently Asked Questions
How do I check whether two images are duplicates?
Load both images and the tool computes a perceptual hash for each, then measures the Hamming distance between them. A small distance means the images are visually the same — even if one was resized or re-saved — while a large distance means they differ.
What is the difference between aHash, dHash, and pHash?
aHash (average) is the simplest and fastest, dHash (difference/gradient) is robust to brightness shifts, and pHash (perceptual, DCT-based) is the most accurate against compression and minor edits. Comparing all three gives a well-rounded verdict.
Can it catch a resized or re-compressed copy?
Yes. That is the point of perceptual hashing: it captures the overall structure of an image, so a thumbnail, a re-encoded JPEG, or a slightly edited copy still hashes close to the original.
How is this different from a pixel diff?
A pixel diff requires identical dimensions and flags any exact change. Perceptual similarity tolerates resizing and compression and answers a different question — do these look like the same image? — rather than are they byte-identical?