AI-like draft
“In today’s rapidly evolving landscape, organizations must embrace innovation while maintaining a balanced approach.”
Generic framing and even sentence structure can raise a weak stylistic signal, but they do not prove AI authorship.
Paste a passage to review patterns associated with AI-assisted writing.
Up to 2,000 characters. Screening aid, not proof of authorship.
Decision warning
Do not use the result by itself to accuse a student, reject a candidate, penalize a writer or enforce a contract. Review drafts, sources, revision history and the author’s explanation.
“In today’s rapidly evolving landscape, organizations must embrace innovation while maintaining a balanced approach.”
Generic framing and even sentence structure can raise a weak stylistic signal, but they do not prove AI authorship.
“At 6:40 on Tuesday, the bakery shutter was still down, so I rewrote the opening paragraph on the bus.”
Concrete personal detail can look human, but an AI model can imitate it. Context and revision history still matter.
A generated outline may be rewritten sentence by sentence, while a human draft may be polished by an assistant.
Mixed authorship is the hardest case and should usually be treated as uncertain rather than forced into a binary label.
These are illustrative patterns, not benchmark samples or guaranteed classifications.
DeepFakeCheck's AI text detector reviews up to 2,000 characters for writing patterns associated with AI assistance. It returns a risk assessment, not a verified authorship probability. No signup is required; submitted text is retained for up to 7 days, then deleted.
The current service uses a language model prompted to review stylistic evidence. It does not run a separate perplexity meter or claim a mathematical proof of authorship. The review considers:
A polished human passage can resemble model output, and an edited model draft can resemble human writing. Translation, accessibility tools, grammar correction and non-native writing can also change style. The result cannot reliably name which model wrote a passage or prove that a person cheated.
We need to be candid: text detection is the least reliable of the four modalities we support. Carefully edited AI output, mixed AI/human passages, translated text, and writing from non-native English speakers can all produce false positives. We recommend using the detector as one input among several — never as a sole basis for accusing someone of academic dishonesty or contract violation.
Up to 2,000 characters. A longer, coherent sample usually gives more context than one sentence, but length does not make the result definitive.
The model can review multilingual text, but reliability varies by language, genre, and editing history. Do not compare scores across languages as if they were calibrated equally.
Mixed or heavily edited passages are the hardest case. Treat them as uncertain and use drafts, sources, and revision history instead of forcing a yes-or-no conclusion.
No. Similar style can come from different models and from human editing. The result does not name a model.
Submitted text is retained for up to 7 days to evaluate and improve detection quality, then automatically deleted.