ChatGPT detector
Evaluate ChatGPT-style writing with a multimodal detection workflow that returns confidence scores, forensic indicators, and a plain-language explanation. The goal is not to make a binary claim in isolation, but to give reviewers evidence they can audit.
Report example
ChatGPT-style writing authenticity report
- Model-like phrasing
- Low-variance sentence rhythm
- Generic transition density
- Human edit and rewrite analysis
Search intent
Commercial search intent for ChatGPT-style writing authenticity checks
Primary evidence
Model-like phrasing, Low-variance sentence rhythm, Generic transition density
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
What the detector evaluates
ZeroTrue combines model-level signals with media forensics so a review team can separate high-confidence synthetic patterns from weak or ambiguous evidence.
- Model-like phrasing
- Low-variance sentence rhythm
- Generic transition density
- Human edit and rewrite analysis
How confidence should be read
Detection output is probabilistic. A high confidence score means the observed indicators are consistent with synthetic or manipulated media, not that a user should be penalized automatically.
- Review score bands with the supporting indicators.
- Escalate borderline cases to manual review.
- Store the explanation and timestamps for audit trails.
Recommended review workflow
Use the detector as a triage layer before policy enforcement, fraud escalation, or editorial decisions.
- Run the file or text through the API or web scanner.
- Inspect evidence regions, timestamps, metadata, and model signals.
- Apply your policy decision outside the detector result.
Use cases
Essay review
UGC moderation
Policy and compliance document checks
Sample report preview
Media preview
Safe sample, redacted upload, or generated demonstration asset.
Public reports should only expose media that is lawful, consented, and safe to publish.
Confidence
Synthetic likelihood: 87%
Model-like phrasing
Evidence item linked to score calibration, source context, and known uncertainty.
Low-variance sentence rhythm
Evidence item linked to score calibration, source context, and known uncertainty.
Generic transition density
Evidence item linked to score calibration, source context, and known uncertainty.
Human edit and rewrite analysis
Evidence item linked to score calibration, source context, and known uncertainty.
Evaluation table
| Criterion | What to check | Why it matters |
|---|---|---|
| Coverage | Text, image, audio, video, code. | Synthetic media risk rarely stays in one format. |
| Explainability | Score, indicators, timestamps, metadata, limitations. | Reviewers need evidence, not a black-box verdict. |
| Accuracy risk | False positives, false negatives, calibration. | High-impact workflows require documented uncertainty. |
| Workflow fit | API, batch, reports, retention, reviewer queues. | Search traffic must convert into a usable product path. |
Methodology and limitations
How to read the score
Detection output should be read as calibrated evidence. A high score means the observed signals are consistent with synthetic or manipulated media under the current model and sample conditions. It does not prove authorship, intent, or model attribution by itself.
Where review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human-AI content, and unseen generators can reduce confidence. Use human review, source context, and policy thresholds before high-impact enforcement.
Next step
Match the action to the visitor intent: detector pages should lead to a scan, research pages to a downloadable report, enterprise pages to a demo, and developer pages to API keys or playground examples.
FAQ
Can ZeroTrue detect every ChatGPT output?
No detector can guarantee perfect coverage. ZeroTrue reports probabilistic evidence and highlights limitations so reviewers can avoid overclaiming.
Can the result be used as the only enforcement signal?
For high-impact decisions, it should be paired with human review, account history, source context, and policy-specific thresholds.
Does the API return explainable evidence?
Yes. Results are designed to include score bands, detected indicators, and modality-specific evidence that can be logged or shown to reviewers.
How does this fit enterprise workflows?
Teams can use ZeroTrue for pre-moderation, trust and safety queues, fraud review, OSINT verification, and API-based authenticity checks.