26% accuracy
Independent testing shows AI detectors correctly identify AI-generated content only about 26% of the time. That is worse than a coin flip.
Beyond AI detection
Better than AI detectors: cryptographic proof
Stop guessing with 26% accurate AI detectors. Start proving with deterministic cryptographic authentication. Detection guesses; proof knows.
26%
accuracy independent testing reports for AI detectors
None
statistical false positives on signed content
Deterministic
verification for signed content
The problem
Why AI detectors cannot be trusted
Statistical AI detection has fundamental flaws that cryptographic proof eliminates.
False positive crisis
AI detectors flag authentic human-written content as AI-generated at high rates. Students get falsely accused. Writers lose work. Reputations suffer.
The arms race problem
As AI generation improves, detection becomes harder. Pattern matching versus pattern creation: detection keeps losing ground.
Real-world consequences
For publishers
Cannot enforce copyright. Litigation fails on unreliable evidence. Revenue lost to AI companies using content without proof.
For enterprises
False positives damage employee trust. Legal exposure from wrongful accusations. Compliance requirements unmet.
For platforms
User complaints about false flags. Moderation chaos. Brand reputation risk from unreliable tools.
For educators
Students falsely accused. Appeals processes overwhelmed. Academic integrity policies undermined.
The better approach
Cryptographic proof: the superior alternative
Instead of analyzing content after creation, embed proof during creation. Here is how Encypher works differently.
- 1
Authentication at creation
Cryptographic signatures embedded when content is generated, not analyzed after the fact.
- 2
Cryptographic certainty
Proof based on cryptography, not statistical patterns. Deterministic for signed content.
- 3
No statistical false positives
Authentic content is not flagged by a statistical classifier. Trust restored.
- 4
Complete audit trail
Know exactly who created what, when, and what changed.
Authors of the C2PA text-provenance standard
Encypher authored the C2PA standard for unstructured text and co-chairs the C2PA Text Provenance Task Force, building the open foundation for verifiable content.
What you get
What this means for you
Deterministic for signed content
Mathematical proof eliminates guesswork.
No statistical false positives
No classifier flagging of authentic content.
Cryptographically verifiable evidence
Deterministic proof your legal team can evaluate.
Future-proof
Cryptography does not degrade as AI improves.
Complete transparency
A full audit trail of the content lifecycle.
Side by side
AI detectors vs. cryptographic proof
| Capability | AI Detectors | Encypher |
|---|---|---|
| Accuracy | 26% | Deterministic |
| False positives | High | None |
| Method | Statistical patterns | Cryptographic proof |
| When applied | After creation | During creation |
| Proof of origin | ||
| Tamper detection | ||
| Gets better over time | No, gets worse | Yes, math is constant |
Who it is for
Who needs better than AI detection
Publishers
Prove copyright infringement with cryptographically verifiable evidence.
Enterprises
EU AI Act marking support with deterministic verification.
Platforms
Content moderation without wrongful accusations.
AI companies
Publisher compatibility and performance tracking.
We didn't adopt the text-provenance standard. We wrote it.
Built on the C2PA open standard
Encypher authored the C2PA standard for unstructured text and co-chairs the Text Provenance Task Force alongside these member organizations. Logos indicate C2PA membership.
