About Duplicate Image Finder
Phones and cameras quietly accumulate near-duplicate photos: burst shots, accidental double-taps, the same screenshot saved twice from different apps. Duplicate Image Finder detects both kinds locally — exact byte-matches via SHA-256, and visually-similar files via a 64-bit difference-hash (dHash) comparable across files by Hamming distance. The two-pass approach catches recompressions, mild crops and quality changes that a simple file-hash misses, while staying fast and predictable: no AI, no cloud, no probabilistic gimmicks. Pick a strictness threshold, review groups, then export only the unique files as a ZIP.
- No uploads
- Browser-only
- Works offline
- 100% free
How it works
- 1
Drop images
Up to 500 photos per batch. Each one is hashed twice — SHA-256 for exact matches and dHash for perceptual similarity.
- 2
Pick strictness
Identical only (Hamming = 0) for byte-clones, Recommended (≤ 5) for recompressions, Loose (≤ 10) for the same scene.
- 3
Review & export
Duplicate groups are shown side-by-side. Click 'Download N unique' to export the deduplicated set as a ZIP.
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Frequently asked questions
Are my files uploaded to a server?
No. Every tool on SnapToolz runs entirely inside your browser using JavaScript and WebAssembly. Your file is read locally, processed in memory, and the result is offered as a download. Nothing is sent to a server — there isn't one to send to.
What's the difference between exact and near duplicates?
Exact = the file bytes are identical (e.g. you saved the same photo twice). Near = the images look the same but the bytes differ (e.g. one was recompressed, lightly cropped, or saved at a different quality). Both can waste storage; near-duplicate detection catches more cases.
How accurate is near-duplicate detection?
We use a 64-bit difference hash (dHash). At threshold ≤ 5 it reliably catches recompressions and small crops with very few false positives. At ≤ 10 it catches more variations but starts flagging photos of the same scene with different exposures. At ≥ 15 expect noise — review every group.
Why not use AI for this?
Production-grade perceptual hashing (dHash, pHash, aHash) is decades-old, deterministic, runs in milliseconds per image, and is what tools like ImageMagick's compare use. AI-based similarity is overkill for finding duplicates and adds 10-100 MB of model weights with worse repeatability.
Will it work on hundreds of photos?
Yes — the limit is 500 per batch. Hashing runs sequentially with progress feedback and uses ~5 ms per image for the perceptual hash plus 50 ms per MB for the SHA-256.
Does it work offline?
Yes. SnapToolz is a Progressive Web App. After your first visit, the app is cached on your device and every tool keeps working without an internet connection.
Is SnapToolz free?
Yes — every tool is 100% free with no sign-up, no watermark, no hidden tier. The whole platform is open source and we have no plan to gate features.