Public forensic reports
Public reports are the evidence archive behind a synthetic media authority platform. Each report should include a safe media preview, confidence score, forensic indicators, metadata, timestamps, limitations, and links to methodology.
Report example
Archive quality gate
- Unique media context
- Safe to publish
- Evidence included
- Methodology linked
Search intent
Public forensic report archive
Primary evidence
Confidence scoring, Metadata analysis, Technical breakdown
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
Archive structure
A report archive should contain only unique, useful, legally publishable examples. The goal is to become a trusted reference source, not a collection of thin pages.
- Image analysis reports
- Deepfake voice reports
- Video authenticity reports
- Text and code provenance examples
Indexability rules
Public reports should be indexable only when they add unique evidence, clear explanation, and responsible context.
- No private uploads
- No duplicate auto-generated pages
- No unsupported accusations
- Methodology linked from every report
Authority value
A living report archive helps search engines and AI answer systems understand that ZeroTrue is an active forensic platform with real evidence assets.
- Stable citations
- Forensic indicators
- Timestamped examples
- Research hub integration
Use cases
Journalism reference archive
Enterprise reviewer training
Backlink-worthy forensic examples
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
Publishable: yes
Unique media context
Evidence item linked to score calibration, source context, and known uncertainty.
Safe to publish
Evidence item linked to score calibration, source context, and known uncertainty.
Evidence included
Evidence item linked to score calibration, source context, and known uncertainty.
Methodology linked
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
Are all reports public?
No. Only safe, lawful, non-sensitive examples should be public and indexable.
Why create public reports?
They turn product activity into a trusted evidence archive that can earn citations and backlinks.
What prevents report spam?
A quality gate requiring unique examples, unique explanation, useful metadata, and responsible limitations.