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Tanod imagepeek – Perceptual Hash
x402
Tanod imagepeek – Perceptual Hash is a paid API for AI agents from tanod.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-11).
Computes perceptual hashes (aHash, dHash, pHash, wHash) for one image and optionally compares them against a second image to return Hamming distances.
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns one or more perceptual hash values (aHash, dHash, pHash, wHash) as hex strings for the input image. When a second comparison image is provided, also returns pairwise Hamming distances between each hash, indicating the degree of visual similarity (lower distance = more similar).
Example use cases
Duplicate image detection in a media library
“I have two image URLs — https://cdn.mysite.com/img_original.jpg and https://cdn.mysite.com/img_resized.jpg — can you run perceptual hashes on both and tell me their Hamming distance so I can see if they're near-duplicates?”
Detecting reposted or slightly cropped images
“Someone might have reposted my photo with a slight crop. Here's my original: https://mycdn.com/original.png. Can you hash it and compare it to https://suspectsite.com/upload.png using all four perceptual hash algorithms?”
Image fingerprinting for content moderation pipeline
“I need to fingerprint this image for our moderation system — can you compute all four perceptual hashes (aHash, dHash, pHash, wHash) at hash size 16 for https://uploads.example.com/user_submitted.jpg?”
Technical details
Endpoint
POSThttps://tanod.dev/v1/image/hash?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need fast, lightweight image similarity detection or deduplication without full ML-based embedding models. It is ideal for near-duplicate detection, tamper/crop detection, or building image fingerprint databases. Prefer it over pixel-diff tools when images may be resized, color-shifted, or slightly cropped, since perceptual hashes are robust to those transformations. Choose it over heavy vision APIs when cost and latency matter and exact pixel fidelity is not required.
Side effects
- Image URL is unreachable or returns non-200 — endpoint cannot fetch the image
- Image exceeds 10 MB size limit — rejected with size error
- Invalid or unsupported image format — decoding failure
- Base64 payload is malformed or exceeds character limit — parse error
- No image provided (both url and image_base64 are null) — validation error
- Compare image also fails to load or decode — comparison skipped or error returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| url | — | Public http(s) URL of the image (at most 10 MB; private, internal and IP-literal hosts are refused); or use image_base64. |
| hash_size | integer | Hash grid: 8 (64-bit hashes, default) or 16 (256-bit). |
| algorithms | — | Default: all four. |
| auto_orient | boolean | Apply the EXIF orientation first (default true). |
| compare_url | — | A second image to compare (or compare_base64). |
| image_base64 | — | The image as standard or url-safe base64 (at most 10 MB decoded; a data:image/...;base64, prefix is fine); or use url. |
| compare_base64 | — | A second image as base64 (at most 10 MB decoded; or compare_url). |
Output
Schema · Response
{
"type": "json",
"example": {
"bits": 64,
"input": {
"bytes": 161713,
"width": 640,
"format": "jpeg",
"frames": 1,
"height": 480,
"animated": false,
"has_alpha": false
},
"notes": [],
"hashes": {
"ahash": "fcffffb310160040",
"dhash": "313c1d66e2e4e595",
"phash": "cedbd88c49eaf808",
"whash": "fcfffff310340000"
},
"source": {
"url": "https://python-pillow.org",
"library": "Pillow",
"license": "MIT-CMU (HPND)"
},
"compare": {
"input": {
"bytes": 159137,
"width": 640,
"format": "jpeg",
"frames": 1,
"height": 480,
"animated": false,
"has_alpha": false
},
"hashes": {
"ahash": "e4fae8c01e0e0000",
"dhash": "0c168a9174dce2be",
"phash": "f1f136161a62d393",
"whash": "eefaf8d03e1e1202"
},
"distance": {
"ahash": 19,
"dhash": 34,
"phash": 34,
"whash": 20
},
"final_url": "https://raw.githubusercontent.com/ianare/exif-samples/master/jpg/gps/DSCN0012.jpg",
"similarity": {
"ahash": 0.7031,
"dhash": 0.4688,
"phash": 0.4688,
"whash": 0.6875
},
"source_url": "https://raw.githubusercontent.com/ianare/exif-samples/master/jpg/gps/DSCN0012.jpg",
"likely_same": false
},
"final_url": "https://raw.githubusercontent.com/ianare/exif-samples/master/jpg/gps/DSCN0010.jpg",
"operation": "hash",
"source_url": "https://raw.githubusercontent.com/ianare/exif-samples/master/jpg/gps/DSCN0010.jpg"
}
}Tags
x402
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