Unified synthetic media detection platform
ZeroTrue helps teams evaluate AI-generated and manipulated media with explainable evidence across text, images, audio, video, and code.
Report example
Multimodal authenticity scan
- Text likelihood elevated
- Image metadata missing
- Voice consistency anomalies
- Video timestamp artifacts
Search intent
Product platform overview
Primary evidence
Modality-specific scoring, Evidence summaries, Public benchmark structure
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
One system for multiple media types
Most authenticity workflows eventually touch more than one modality. ZeroTrue keeps scoring, evidence, and reporting consistent across media types.
- Text and LLM writing
- Images and deepfakes
- Voice clones and synthetic audio
- Video manipulation and generated code
Explainability for review teams
Results are designed to be inspected, logged, and escalated instead of treated as a black-box verdict.
- Confidence bands
- Forensic indicators
- Metadata and provenance notes
- Reviewer-ready summaries
Built for integration
The platform supports web review and API-first deployment for enterprise pipelines.
- Unified API schema
- Async processing for large media
- Batch workflows
- Audit-friendly reports
Use cases
Trust and safety review
Fraud and impersonation checks
Newsroom and OSINT verification
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 risk: high
Text likelihood elevated
Evidence item linked to score calibration, source context, and known uncertainty.
Image metadata missing
Evidence item linked to score calibration, source context, and known uncertainty.
Voice consistency anomalies
Evidence item linked to score calibration, source context, and known uncertainty.
Video timestamp artifacts
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
What does ZeroTrue detect?
ZeroTrue focuses on synthetic and manipulated text, images, audio, video, and code.
Is ZeroTrue only an AI text detector?
No. The core advantage is multimodal detection across several synthetic media categories.
Can developers integrate it?
Yes. ZeroTrue includes API-first workflows for product and review pipelines.