Signals, scores, and limits

How do AI detectors work—and why can they fail?

GenDetect defaults to local provenance and supporting-evidence checks. When configured, full analysis uses open models and specialist services. These models are not yet calibrated on our business data; a screening index is not an AI probability, and insufficient evidence still requires human review.

Current version
Local evidence and optional full analysis
Last updated

01 · Implemented signals

What does the current version analyze?

Each input type follows a different browser analysis path. This page describes signals the code actually reads and does not present unimplemented model capabilities as product features.

02 · Score meaning

The screening index is not a source probability.

The new interface does not combine text, image or frame rules into one score. Each model output is shown independently; it is not a calibrated probability or statistical confidence interval.

Heuristic aggregation

High repetition, limited color diversity and unusual edges are supporting heuristic clues only. Metadata, provenance verification and model outputs are not summed or averaged into an AI probability.

Sparse evidence causes abstention

Text with fewer than 40 analyzable tokens returns insufficient evidence. Images below 128 pixels on either edge also abstain unless a generator metadata claim is present. If video frames cannot be decoded, the result falls back to metadata facts only.

Rules do not attribute a model

The current result cannot identify a generation model, author, or editing process. Similar statistical patterns may appear in templated human writing, compressed media, and AI-generated content.

03 · Known limitations

What can cause a wrong result?

Any detector that sees only the final artifact faces lost information and distribution change. The cases below can create both false positives and false negatives.

Rewriting, cropping, and transcoding

Human rewriting, translation, screenshots, filters, crops, platform compression, and repeated transcoding all alter original signals. They may hide generation traces or create patterns that look unusual.

New models and deliberate evasion

Generation systems keep changing, and people can deliberately adjust rhythm, noise, or encoding parameters. Fixed rules cannot cover generation methods that have not yet been observed.

Natural content is irregular

Code, poetry, tables, brand templates, low-light photos, and illustrations may naturally cross text or image thresholds. Unusually encoded video only produces metadata warnings and never adds a generation score. A high index is not proof, and a low index does not prove human authorship.

04 · Verification and privacy

Put the result back into an evidence chain.

Reliable conclusions come from provenance, timing, and editing history, not one score. The current implementation keeps input on the device and commits to disclosing future capability changes.

Start with the original file

Preserve complete text, the original file, publication time, author information, and edit history. Screenshots and reposts discard provenance signals that could support verification.

Cross-check independent evidence

Use content credentials, reverse-image search, trusted-source versions, audiovisual continuity, and contextual facts. Important decisions require human review and a chance for the person being assessed to explain.

Local by default, with remote image modes disclosed

Text, video, and the default image heuristic run in the browser. An image is uploaded only after the user selects SynthID, SD/Flux, or an available external Verify mode. External Verify may also forward it to the provider named in the interface; the interface and privacy notice state purpose, retention, and deletion.