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.
Free AI Text Detector
Paste a passage to review patterns associated with AI-assisted writing.
Up to 2,000 characters. Screening aid, not proof of authorship.
Paste articles, chat logs, or any text content to analyze for AI generation patterns...
0 / 2000 characters
Analyze Text
Paste articles, chat logs, or any text content to analyze for AI generation patterns...
0 / 2000 characters
Analyze Text
Text Not RetainedCurrent request only
Up to 2,000 charsLonger samples help
Use With ContextNever sole evidence
Decision warning
A text score cannot prove who wrote it
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.
Human-specific draft
“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.
Mixed or edited text
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.
AI Text Detector — Was This Written by AI?
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, usage limits apply, and submitted text is not retained.
How AI text detection works
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:
- Structure: unusually rigid transitions, repeated templates and consistently even sentence patterns.
- Specificity: generic claims without concrete experience, source detail or verifiable context.
- Internal consistency: citations, examples or factual patterns that conflict with the surrounding passage.
What the result cannot establish
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.
Common use cases for text detection
- Teachers and academic integrity offices screening student submissions — paired with a conversation with the student, not as a sole judgment.
- Editors and content managers reviewing freelance submissions for undisclosed AI use.
- Recruiters evaluating cover letters and writing samples.
- Publishers and PR teams spotting AI-generated press releases or astroturf reviews.
- Marketplace trust teams identifying AI-generated product descriptions or fake reviews.
Honest limits of AI text detection
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.
Text detection FAQ
How much text can I paste? Up to 2,000 characters. Longer, coherent samples usually provide more context than a single sentence, but length does not make the result definitive.
Can it review other languages? The model can review multilingual text, but reliability varies by language, genre and editing history. Avoid comparing scores across different languages as if they were calibrated equally.
What if AI output was edited? Mixed or heavily edited passages are the hardest case. Review them as uncertain and use drafts, sources and revision history instead of forcing a binary conclusion.
Can it tell which AI model wrote the text? No reliable model-family attribution is currently provided. Similar style can be produced by different models and by human editing.
Is my text stored? No. Submitted text is held in memory long enough to run the analysis, then discarded. We do not log, store, or train on it.