Duplicate Image Finder — Find Exact and Similar Photos in a Folder
Find exact and visually similar duplicate images in a folder. Perceptual hashing detects rotated, resized, recompressed copies. Free, in-browser.
About Duplicate Image Finder
A duplicate image finder scans a set of images and groups exact and visually similar duplicates together — using both byte-level hashing (catches identical files copied to multiple folders) and perceptual hashing (catches resized, recompressed, slightly cropped, or rotated copies) — so you can reclaim disk space, deduplicate a photo library, or audit a content folder before uploading it to a CMS or marketplace. The ZTools Duplicate Image Finder runs entirely in the browser using a fast perceptual hash (pHash / dHash) over a sampled grid of every image, surfaces clusters of near-duplicates with a similarity score, and lets you preview side-by-side before deciding which copies to keep.
Use cases
- Cleaning a phone photo backup. Years of phone backups accumulate burst-shots and forwarded WhatsApp copies of the same picture. Find the clusters and keep one master per group.
- Catalogue audit before marketplace upload. Sellers sometimes accidentally upload the same product photo twice or a slightly cropped variant. Run the duplicate finder before the catalogue goes live.
- Reclaiming disk space. Designers keep multiple versions of the same hero image. Find duplicates, delete redundant copies, save 50%+ of folder size.
- Plagiarism / re-use audits. Compare a folder of submissions against a reference library to spot copied or lightly-edited reuse.
How it works
- Drop a folder of images. JPG, PNG, WebP, HEIC. The browser reads files locally — nothing uploads.
- Compute hashes. Byte-level SHA-256 for exact-match detection. pHash / dHash on a 32x32 greyscale sample for perceptual similarity.
- Cluster by similarity. Hamming distance between perceptual hashes groups near-duplicates. Threshold slider chooses how strict similarity must be.
- Review clusters. Each group shows thumbnails side-by-side with file size, dimensions, and similarity %.
- Mark and act. Tick which copies to keep / delete. Export a CSV of decisions; or download a ZIP of survivors.
Examples
Input: 500 phone photos including 80 burst-shots
Output: ~30 clusters; e.g. one cluster of 7 near-duplicate burst-shots of the same scene
Input: Marketplace catalogue, 200 product photos
Output: 5 clusters of accidentally re-uploaded products
Input: Original + resized + recompressed copy
Output: All three flagged as the same cluster at >95% similarity
Frequently asked questions
How does it detect resized or rotated duplicates?
Perceptual hashing reduces each image to a small grayscale grid and hashes the pattern. Resize, recompression, and small rotations preserve the pattern, so the hashes stay close.
What similarity threshold should I use?
Hamming distance ≤ 5 (about 95% similar) is safe for "almost certainly the same". 6–10 catches more aggressive crops; > 10 starts including merely "thematically similar" photos.
Will it detect mirrored copies?
Optional — a "check flips" toggle hashes mirrored variants too. Slows the scan.
Does it actually delete files?
No — the tool only flags duplicates and lets you mark decisions. You delete locally on your file system. Safer that way.
How big a folder can it handle?
Tested up to ~5,000 images on a modern laptop. Larger libraries should be batched per folder.
Are RAW files supported?
JPG previews extracted from RAW are usually compared. Pure RAW (CR2, NEF, ARW) decoding is limited; convert to JPG first for best results.
Pro tips
- Run on a copy of the folder until you trust the tool — then run on the master once.
- Keep the highest-resolution / most-recent copy; delete the rest.
- Tighten the threshold to ≤ 5 for safety; loosen only when you actively want to find re-edits.
- For catalogue audits, export the CSV and review before deleting — accidental deletes are painful.
- Re-run periodically — duplicates accumulate quietly over time.
Reviewed by Ahsan Mahmood · Last updated 2026-05-05 · Part of ZTools.
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