AI Content Detector

Accuracy varies by detector and text type. Statistical methods (perplexity/burstiness) achieve 70-85% accuracy on long-form English text. ML-based classifiers can reach 90%+ on unedited AI output but drop to 60-70% on paraphrased or edited text. No detector is 100% reliable.

Common approaches include: perplexity scoring (how predictable each word is), burstiness analysis (variation in sentence complexity), entropy measurement (randomness distribution), and trained ML classifiers that learn statistical signatures of AI-generated text.

Most detectors identify AI-generated text generally rather than attributing it to a specific model. Some research classifiers can partially distinguish between models based on different statistical fingerprints, but this is not reliable in practice.

Accuracy decreases significantly below 200-300 words. Most detectors need enough text to establish statistical patterns. A single paragraph is generally too short for reliable detection. Results on short text should be treated as unreliable estimates.

Yes, significant human editing, paraphrasing, and restructuring can reduce detector confidence. This is a fundamental limitation — as AI text becomes more human-like and is blended with human editing, the boundary becomes increasingly blurred.