EditSpot — find hints of editing in an image
▶ Use it now: https://ronsuthlor3-lab.github.io/editspot/
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Shows where an image may have been edited. Runs entirely in the browser. Images are never uploaded. Free, no sign-up.
⚠️ Hints, not proof
Every analysis here only shows that "this area behaves differently from the rest of the image". Edits cause that,
but so do text, sharp edges, different textures and resizing. Never judge or accuse anyone on this alone.
Views
| View | What it shows | Limits |
|---|
| ELA | Re-saves as JPEG and shows how much each area changed | Best on JPEGs not re-saved many times. Edges/text are naturally bright. |
| Noise | Fine-grain noise per 8×8 block; pasted or blurred areas often differ | Flat areas (sky) are naturally low |
| Copy-move | Areas that appear twice in one image (clone stamp, duplicated objects) | Misses copies of flat, featureless areas; natural repeats (bricks, tiles) may be flagged |
| File info | Camera, capture time, software, estimated JPEG quality, editor traces | Social media usually strips this |
| Inkseal | If sealed with Inkseal: authentic/modified and the hidden creator ID | — |
Copy-move test results (synthetic test images saved as JPEG q70–85): every planted copy was found, including a
60×50 px patch, with zero false alarms on 16 unedited images. Real photos are harder than test images, so treat this as
indicative, not a guarantee.
How it works
src/analysis.js — ELA difference, high-pass noise map, and copy-move detection (downscaled grey image, 8×8 blocks
described by mean + 4×4 cell pattern, lexicographic sort with tolerant neighbour comparison, shift-vector voting with
sub-pixel bin merging, flat blocks skipped).src/jpeginfo.js — EXIF (make, model, software, dates) and quality estimation against scaled IJG tables.src/inkseal/ — copied from Inkseal to read seals and hidden IDs.
Run locally with any static server. Tests: npm test (Node 20+).
License
MIT. Part of an artist toolkit with Inkseal, SlipCheck and ArtQueue.
