The Deep AI Detector blog — AI detection research and how-tos
Research notes, accuracy benchmarks, and practical how-to guides for AI content detection. Written for teachers, editors, publishers, and developers who need detection that holds up. Subscribe to get the first posts when they land.
What we're writing about
- Catching paraphrased AI: a benchmark — how Deep AI Detector finds AI rewrites that other detectors classify as 97%+ human.
- Training on 2 billion+ samples: what it actually means — why corpus size matters more than model size for detection accuracy.
- Defending a verdict in academic appeals — how to use per-sentence highlights in an integrity hearing.
- Multilingual AI detection that just works — paste in any of 20+ languages, get a verdict for the original text.
- What "honest about edge cases" actually means — when we refuse to give a confident verdict, and why that's the right call.
- Building AI detection into your product — a developer's guide to the REST API, webhooks, and bulk batch endpoints.
Who reads Deep AI Detector content
- Teachers and academic integrity offices looking for evidence-backed approaches to suspected AI-assisted student submissions.
- Editors and publishers needing repeatable processes for verifying freelance copy and submitted manuscripts.
- Content teams and agencies auditing freelance writer output against AI-disclosure clauses.
- Platform trust + safety leads deploying AI detection at scale against AI-generated reviews and UGC.
- Developers integrating AI content detection into existing apps and workflows through the REST API (Pro tier and above, launching soon).
What the Deep AI Detector blog will cover
AI detection is moving fast. New language model families ship every few months, paraphrasers keep improving, and the question of how to defend a verdict in front of a person — a student in an academic appeal, a freelance writer in a contract dispute, a candidate in a hiring review — only gets harder. The Deep AI Detector blog will tackle these problems from the perspective of the team that ships the detector, not from the perspective of a content marketer trying to rank for "best ai detector free no signup."
The editorial promise
Every post will include either reproducible numbers, a defensible methodology, or both. We will not publish "AI detection accuracy is over 99%" claims, because that number is contextual — it depends on the model that generated the text, the language, the writing level, and whether the text was rewritten by a paraphraser. We will publish what we measured, on what data, and what fails. When we are wrong about a sample, we will say so and explain why.
Topics we will not cover
No drama posts about whether AI detectors should exist. No takedowns of competing detectors. No marketing testimonials dressed up as case studies. The blog is a research resource for the people doing the detection work, not a content funnel.
How does an AI detector actually work?
An AI content detector reads a text passage and estimates the likelihood that it was generated by a large language model rather than written by a human. Modern detectors combine multiple signals: distributional patterns in word choice and sentence structure (LLMs tend to generate text with measurable statistical regularities that human writing does not have), surface-level vocabulary patterns trained against a large corpus of known AI and human text, and rhythm signals — the variance in sentence length and lexical diversity that humans naturally produce and LLMs flatten. Deep AI Detector trains on a 2 billion+ sample corpus and returns per-sentence verdicts, so reviewers can see exactly which lines drove the result.
Can teachers detect ChatGPT in student submissions?
Yes — and the per-sentence verdict matters more than the overall score. An overall "78% AI" result is not enough to defend in an academic integrity hearing. Per-sentence highlighting that shows which specific sentences triggered the AI verdict is. That is the evidence a reviewer can share with the student, the academic committee, and the dean. Posts in this blog will walk through real (anonymized) review workflows used by academic integrity offices, including how to handle the cases where the detector is wrong.
Are AI detectors accurate on paraphrased text?
Most are not. The standard failure mode of legacy AI detection tools is to read paraphrased AI (text run through a tool like QuillBot, Undetectable.ai, or a custom prompt) as 97%+ human. Deep AI Detector is tuned specifically for these cases — they are the hardest cases in the field and they are where detection accuracy actually matters. Our research posts will publish per-sample breakdowns of where we win and where we still lose, with the anonymized samples available for verification.