Surface profile
Color indicates the magnitude of each measurement only: green is low, red is high. It is not a human/AI scale.
AI detector. AI humanizer. Advanced Scan. Authorship Verification. Nearly 100% accurate. The web is full of impressive labels attached to a box that asks you to upload your work.
resnoop is the nosy alternative. Drop in a file and it makes that file talk — on your machine, inside your browser, without sending the document anywhere.
No account. No upload. No remote analysis API quietly receiving your novel, contract, photograph or half-finished manifesto. The analysis engines run on your device.
Even the import libraries are bundled with resnoop and work locally. Word, PDF, EPUB, HTML, RTF, CSV, XML, Markdown and plain text are opened in the browser; ZIP archives are mapped without first being unpacked onto your disk.
The website delivers the application. After that, your processor does the snooping. Clear the session and resnoop forgets the file because it was never a file repository in the first place.
Extensions can lie. resnoop checks the file signature, calculates its SHA-256 hash and looks at what is actually inside: metadata, Content Credentials, hidden characters, document structure, decoded pixels and writing patterns.
It does not flatten all of that into one mysterious percentage and hide the workings. The clues stay visible, including the awkward ones that disagree with each other.
For Italian and English prose, resnoop builds a deterministic writing profile and places it on a Human-like → AI-like scale. It measures rhythm, repeated openings, recurring wording, punctuation, formulaic transitions, entropy, predictability, redundancy, compression, Zipf fit, character n-grams, sentence-length behaviour and changes between neighbouring sections.
Long documents are not fed to an average-shaped blender. Their headings and sections become anchors, so you can see where the voice is stable, where it shifts and which passages pull the overall result.
The number is a compatibility index, not a confession. Formal prose can look generated; edited AI text can look human; translation, genre, quotations and broken extraction can move everything. When the evidence is not good enough, resnoop says so.
If the text is yours, edit the Markdown and recalculate immediately. resnoop keeps the before-and-after measurements so you can see whether the prose actually changed or merely swapped one tic for another.
It can also build a humanization prompt from the measurements it has already calculated, giving an editor precise revision priorities without pretending that another model can determine authorship.
Short documents can travel inside the prompt. Long ones become a guided block-by-block session. The model gets the diagnostics; the final editorial choices remain yours.
Provenance is what the file declares. If C2PA Content Credentials are present, resnoop reads the manifest locally, checks the content binding and signature it can verify, and shows exactly which checks were and were not completed.
Pixels are what survived. Entropy, contrast, colour, edges, residual noise, repeated blocks, compressibility and frequency energy describe the visible surface. They can reveal structure and transformation, but they cannot magically prove who — or what — made the image.
Need a shareable copy without the luggage? resnoop can produce a new PNG from decoded pixels only, leaving out EXIF, IPTC, XMP, GPS, Content Credentials and container metadata. Visible watermarks, naturally, remain visible.
resnoop can measure rhythm, predictability, formulaicity, entropy, redundancy, structure and anomalies. It can compare versions of the same text and show exactly what a transformation changed.
But the final leap — from this text has these properties to this text was written by AI — is not licensed by mathematics. A carefully prompted model, a thorough edit or an unusual Human voice can make provenance disappear from the surface.
These are powerful instruments, not a truth machine. The useful future may not be knowing who wrote a text, but seeing precisely how it was built and how it changed. Less spectacular than a verdict. More honest.
Never use this score alone to accuse, penalize or make decisions about a person. Authorship cannot be proven — or disproven — from the surface of a document.
The clean PNG contains only decoded pixels. EXIF, IPTC, XMP, GPS, C2PA and container metadata are not copied. Visible or pixel-encoded watermarks remain.
Image
Color indicates the magnitude of each measurement only: green is low, red is high. It is not a human/AI scale.
These measurements describe the decoded pixels only. They do not estimate whether an image is human-made or AI-generated. Editing, resizing and compression can change every value.
Hover a signal name for its definition, score weight, reliability and known confounders. Values are normalized by text length or sentence coverage; a single tic never decides the result by itself.
Hover a metric name for its definition, range, relationships and role in detection. Directional color is shown only when the active reference supports it.
All locally calculated V10 measurements are exposed here. Model marks features used by the installed calibration reference; diagnostic marks measurements retained for analysis or ablation. These values do not replace the deterministic score.
MΔ is the within-document regularized Mahalanobis distance of a section; Δ↔ is the multivariate jump from the preceding comparable section. They describe internal change only — quotations, translation, register changes and editorial material can all produce it.
The deterministic components that contribute to the score are shown here.
The index is intended for reproducible comparison and is not proof of authorship.
The scale describes surface-style compatibility, not authorship or provenance. Green does not prove human writing and red does not prove the use of an LLM.