Educational guide

How AI detectors work

This guide explains how to evaluate AI detector systems without relying on a single visual clue or detector score. The strongest workflow combines provenance, forensic signals, context, and a documented review decision.

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Coverage

Text, images, audio, video, and code in one product.

Access

A web checker for direct use and an API for integrations.

Interpretation

Probabilistic evidence that should be reviewed with source context.

First-pass checks

Start with indicators that are fast to inspect and low-risk to document.

  • Feature extraction by modality
  • Model-based classification
  • Confidence calibration
  • Evidence explanation for reviewers

Detector-assisted review

Use a detector to identify patterns that are hard to inspect manually, then validate the output with source context.

  • Check whether evidence is localized or global.
  • Compare detector confidence with metadata and provenance.
  • Keep borderline cases in a manual review queue.

When not to overclaim

Compression, editing, templates, translation, and human post-production can create signals that resemble synthetic artifacts.

  • Avoid public accusations from a single automated result.
  • Use confidence bands, not absolute language.
  • Document what evidence was present and what was missing.

Common use cases

Newsroom verification before publication.

Marketplace or platform review of suspicious media.

Enterprise fraud and impersonation triage.

Methodology and limitations

How to read a result

Detection output is probabilistic evidence. A high score means the observed signals are consistent with synthetic or manipulated content under the current model and sample conditions. It does not prove authorship or intent.

When review is required

Short samples, heavy editing, compression, translation, re-recording, mixed human and AI content, and new generators can reduce confidence. Use human review before high-impact decisions.

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Frequently asked questions

Can manual inspection replace detection tools?

Manual inspection is useful, but many synthetic signals are subtle or hidden in metadata, frequency patterns, or frame-level inconsistencies.

What should I do with an uncertain result?

Preserve the evidence, request source material when possible, and route the case to human review instead of making a final claim.

Why do detectors disagree?

Detectors use different training data, features, thresholds, and modality coverage, so disagreement is expected on ambiguous samples.