Humanization detection
Humanization detector — spot AI text that’s been rewritten.
AI humanizers and “undetectable AI” tools rewrite ChatGPT output to evade detection. They’re effective against generic AI detectors but leave their own distinctive fingerprints. This detector finds those fingerprints.
Last reviewed 2026-05-11 · See methodology
Signal model
What the humanization detector looks for.
- Register collisions. Real writers settle on one tone — formal or casual. Humanizers stitch contractions onto academic connectors, producing the most reliable fingerprint.
- Humanizer filler vocabulary. “Bottom line”, “worth noting”, “plus,” “then again” — these are exactly the phrases humanizer tools substitute for LLM-typical transitions. Their density gives the tool away.
- Engineered sentence-length variance. Humanizers deliberately scramble the even sentence cadence of LLM output. The result is high variance combined with uniform topical structure — a combination real writers rarely produce.
- Casual-replacement markers. “Plenty of”, “lots of”, “figure out” — downgrades from formal LLM vocabulary that humanizers apply systematically.
- Residual LLM structure. Phrases like “in summary” or “as previously mentioned” that humanizers commonly miss. When they appear next to humanizer-typical fillers, the combination is conclusive.
When to use the humanization detector
- EducatorsWhen a student essay scores ambiguously on a generic AI detector, check whether it shows humanizer markers — a stronger signal than the AI score alone.
- EditorsBefore publishing freelance submissions, check for the patterns that indicate the writer ran ChatGPT output through a humanizer rather than drafting from scratch.
- ResearchersStudy the cat-and-mouse between humanizers and detectors. Run the same input through both tools to see how transformations affect detection.
Run the other side
Try the AI Humanizer to see what the detector is catching.
Run a piece of AI text through the humanizer, then paste the output back into this detector. You’ll see the specific transformations flagged in the signal breakdown.
Humanization detector — common questions
What is a humanization detector?
A humanization detector identifies the specific patterns AI humanizers leave behind when they rewrite LLM output to look more human. These are different signals than a general AI text detector — they look for the rewrite, not the original generation.
How is this different from a regular AI text detector?
A regular AI detector asks: does this look LLM-generated? A humanization detector asks: does this look LLM-generated and then run through a rewriting tool? The signals are different — humanizers often defeat AI detectors but leave their own distinctive marks.
Can it tell which humanizer was used?
Not reliably. Most humanizer tools converge on similar transformations (contractions, transition swaps, sentence restructuring), so the rewrites look similar across tools. We flag the patterns, not the tool.
What does the score mean?
0–29: likely original (LLM or human, but not humanized). 30–59: possibly humanized. 60–100: strong humanizer markers detected. As with any detector, treat the score as a probability and combine it with context.
Is the humanization detector free?
Yes, free and browser-side. The text you paste does not leave your device.