Updates, guides, and deep dives on AI content detection.
A breakdown of the AI-generated content types our ensemble models can identify — and the signals they look for.
GPT-family models produce text with distinctive statistical signatures: low perplexity, uniform sentence lengths, heavy use of transition words ("furthermore," "moreover," "it is important to note"), and formulaic paragraph structures. Airno's ensemble detects these patterns across short and long-form content.
Anthropic's Claude models tend to produce well-structured, cautious text with characteristic hedging patterns ("it's worth noting," "I should mention"). While stylistically different from GPT, Claude text still exhibits the statistical regularity and vocabulary patterns that our detectors flag.
Paraphrasing tools like QuillBot, Wordtune, and built-in LLM rewriting features take human text and smooth it out. The result often loses natural burstiness — the variation in sentence length and complexity that marks genuine human writing. Our statistical detectors pick up on this flattening effect.
AI-generated images leave forensic traces invisible to the human eye: frequency domain anomalies, inconsistent noise patterns, metadata artifacts, and telltale generation signatures. Airno's image pipeline combines CNN-based detection with metadata analysis and frequency domain checks.
No AI detection tool is 100% accurate. Detection confidence varies with text length, writing style, and the specific model used to generate content. Very short texts (under 30 words) lack sufficient signal for reliable detection. Airno provides confidence scores and highlights specific evidence — the final judgment is yours.