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Halowerk Image Duplicate Detector
x402
Halowerk Image Duplicate Detector is a paid API for AI agents from medien.halowerk.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Compares 2–8 images using three perceptual hashing algorithms (average hash, gradient hash, DCT hash) to detect duplicates and near-duplicates across each pair
Price / call$0.005
Success—
StatusUnknown
How this service works
Takes between two and eight images by URL or inline base64 and compares each pair. Three perceptual hashes are computed, because each fails differently: an average hash over an 8x8 grey image is cheap but moves with brightness, a gradient hash reads the slope between neighbouring pixels and survives exposure changes, and a DCT hash over the low frequencies of a 32x32 grey image is the sturdiest against rescaling and re-encoding.
Output
Returns pairwise comparison results for all submitted image pairs, including three perceptual hash values (average hash, gradient hash, DCT hash) and similarity scores for each. Indicates whether pairs are likely duplicates based on hash distances, allowing the agent to reason about visual identity independent of file format, brightness shifts, or mild rescaling.
Example use cases
Deduplicating user-uploaded photos
“I have four uploaded profile photos from users that might be duplicates — check https://uploads.example.com/img1.jpg, img2.jpg, img3.jpg, and img4.jpg against each other and tell me which pairs are near-identical using all three perceptual hash methods.”
Detecting reused stock images
“Can you check if this blog post thumbnail at https://blog.example.com/header.jpg is visually the same as our competitor's hero image at https://competitor.com/hero.jpg — even if they resized or re-saved it?”
Verifying asset integrity after re-encoding
“We ran our master image through a compression pipeline and got back https://media.example.com/output-compressed.png — compare it to the original at https://media.example.com/original.png and tell me whether the perceptual hashes still match across average, gradient, and DCT methods.”
Technical details
Endpoint
POSThttps://medien.halowerk.com/v1/image-dupeWhen to prefer
Choose this endpoint when you need to detect whether images are visually identical or near-identical despite re-encoding, resizing, brightness changes, or minor compression artifacts — scenarios where byte-level comparison would fail. Prefer it over simple hash or checksum tools when resilience to JPEG re-saves, exposure shifts, or format conversions is required. It is ideal for deduplication pipelines, content moderation, asset management, and detecting reused or plagiarized imagery.
Side effects
- Invalid or inaccessible image URL returns an error for that image
- Base64 payload that is not a valid image format causes a decoding error
- Submitting fewer than 2 or more than 8 images falls outside the supported range
- Network timeout fetching a remote image URL
- Unsupported image format (e.g. raw TIFF or proprietary formats) may fail to decode
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| images | array | Two to eight images. Each takes either url or content_base64. |
| check_mirror | boolean | Also compare against the mirrored image. |
| check_rotations | boolean | Also compare against the other image rotated 90, 180 and 270 degrees. |
| phash_threshold | integer | Override the pHash distance below which a pair counts as likely edited. Default 10. |
Tags
x402
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