About ZeroTrue
ZeroTrue is focused on making synthetic media detection more useful, transparent, and operational for teams that need to review authenticity at scale.
Report example
Trust signal overview
- Limitations explained
- Methodology linked
- Research hub available
- Responsible use guidance
Search intent
Company trust and E-E-A-T
Primary evidence
Public methodology, Research hub, Benchmarks
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
Mission
AI-generated media is becoming normal. The hard problem is deciding when authenticity matters and how to explain detection evidence responsibly.
- Multimodal detection
- Evidence-backed reporting
- Responsible use
- Reviewer-centered workflows
Transparency principles
ZeroTrue avoids unsupported certainty claims and treats detection as probabilistic evidence.
- Publish limitations
- Track false positives
- Document methodology
- Support human review
Where the platform fits
ZeroTrue is used as an authenticity signal in workflows where synthetic media can create fraud, trust, security, or policy risk.
- Enterprise review
- Journalism and OSINT
- Platform integrity
- Developer products
Use cases
Enterprise AI detection
Media authenticity
Trust and safety
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
Transparency: documented
Limitations explained
Evidence item linked to score calibration, source context, and known uncertainty.
Methodology linked
Evidence item linked to score calibration, source context, and known uncertainty.
Research hub available
Evidence item linked to score calibration, source context, and known uncertainty.
Responsible use guidance
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
Why focus on synthetic media?
Synthetic media risk spans text, image, audio, video, and code, so a narrow detector is not enough for many teams.
Does ZeroTrue claim perfect detection?
No. Detection is probabilistic and should be used with context and human review.
Where can I learn about methodology?
Start with the research and benchmarks pages for limitations, metrics, and evaluation structure.