Korean AI detector

This Korean AI detector accepts Korean text, sends it to our server for classification, and returns a description of the properties observable in the writing. It does not return a probability, a percentage, or a statement about who or what produced the text, and the reason is architectural rather than rhetorical.

The request our server makes carries an explicit instruction to the runtime: never claim certainty about authorship, plagiarism, or which AI model wrote text, and describe observable signals and limitations instead. Every constraint described on this page follows from that instruction, which is why the page can state them as facts about the system rather than as promises about our conduct.

The distinguishing feature of a genuinely Korean detector page ought to be that it names Korean phenomena. Two of the Korean-language landing pages competing on this term were read closely for this rewrite and neither identifies a single one, offering instead that they analyse writing patterns and perplexity, which describes nothing about Korean in particular.

The properties below are features of written Korean, not measurements this engine claims to compute individually. They are listed so a reader who knows them can evaluate the returned description, and their own text, without depending on any tool.

Speech-level consistency

Korean encodes politeness grammatically in the verb ending, so register is structurally visible. A document written by one person in one sitting generally occupies one level throughout.

Spacing regularity

Korean 띄어쓰기 rules are sufficiently intricate that highly fluent native writers deviate from them routinely in unedited prose. Uniform conformity across informal writing is anomalous.

Loanword orthography

Standard transcriptions such as 메시지 and 초콜릿 compete in real usage with 메세지 and 초콜렛. Exclusive use of standard forms indicates an edited or generated source.

Particle distribution

The 은/는 and 이/가 alternation carries information structure. Invariant usage across a passage indicates an absence of choices rather than a style.

Terminal ending variety

Extended sequences closing on ~것입니다 or ~수 있습니다 without interruption by shorter constructions.

Sino-Korean abstraction

Density of ~적 and ~화 derivations where native verbal constructions would carry the same content.

Three steps, with the third being the one that determines whether the exercise was worth anything.

Step 1

Submit the Korean text. The server accepts between 1 and 12,000 characters and rejects anything outside that range before any analysis occurs.

Step 2

The request is classified server side and returns a description of the signals present.

Step 3

Evaluate the description against independent knowledge of the document's history. The output is one input to a judgement, never the judgement.

The allowance is five requests per rolling 24 hour window, enforced server side rather than in the page, so clearing browser storage does not restore it. The window rolls continuously: a request stops counting 24 hours after it was made, which means the allowance replenishes incrementally rather than resetting at a fixed hour.

The 12,000 character ceiling is a character count rather than a token or word count, and that distinction matters more in Korean than in most languages. A Korean syllable block occupies one character while encoding what would take two to four Latin letters, so the same ceiling admits substantially more Korean prose than English. A full essay or 리포트 usually fits inside one request.

No, and the more confidently a page asserts otherwise, the less it is worth. Competing pages on this term advertise detection of ChatGPT, GPT-4, Claude and Gemini by name. Finished prose does not carry a reliable signature of its generator, and a classifier reading only the output text has no privileged access to that information.

Our position is enforced rather than promised: the instruction sent with each request forbids the claim outright. An absence imposed by the runtime is verifiable in a way that a disclaimer is not.

One Korean-language competitor read for this page displays 99% accuracy alongside industry-leading detection powered by advanced machine learning models, and separately prices each check at $3.99 with no free tier. Another states that its Korean support has the same accuracy you get in English. Neither publishes a methodology, a test corpus, or a definition of what was being measured.

An accuracy figure without those three things is not a measurement. None has been taken for this route, so none is printed here. That is a narrower claim than any competitor makes, and the only one that survives being asked how it was arrived at.

Detection methods reward variance, and the categories of Korean writing most often submitted for checking are the ones institutionally trained out of it. Template-conforming 보고서, heavily revised 자기소개서 and formal academic prose in 합쇼체 are all required to be regular. Regularity is the property these methods read as machine-like, so the writing that most needs to survive a check is structurally most exposed to one.

The effect is documented beyond Korean. The paper "GPT detectors are biased against non-native English writers" by Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu and James Zou reports that the detectors evaluated consistently misclassified non-native English writing samples as AI-generated while identifying native samples correctly, and that simple prompting strategies both mitigated the bias and bypassed the detectors, which the authors take as evidence that such detectors may penalise writers with constrained linguistic expression. A Korean researcher submitting in English occupies precisely that position.

The asymmetry worth exploiting is that a detector sees only the finished artefact, while the writer has access to the entire process that produced it. Argue from the process.

Document history

한글, Word and Google Docs retain revision history. A growth curve across sessions is evidence of a kind no classifier can generate or rebut.

Intermediate artefacts

Outlines, sources, abandoned sections. Drafting produces waste, and generation does not.

Interrogate the flag

Request the tool name and the reported signals. A percentage unaccompanied by a description is not a proposition that can be contested.

Refuse the rewrite

Deliberately introducing irregularity to defeat a classifier degrades the text and establishes nothing about its origin.