How ai detectors Work: Techniques, Signals, and Limitations Understanding how an ai detector operates requires a look beneath the surface of language models and digital content. Detection systems typically rely on statistical footprints left by generative models: patterns in token probability distributions, repetition or uniformity of sentence structures, and distinctive punctuation or syntax usage. Many detectors analyze features such as perplexity, burstiness, and token-level entropy to estimate whether text was produced by a model rather than a human. For image and multimedia content, detectors examine pixel-level artifacts, compression anomalies, metadata inconsistencies, and cross-modal mismatches that betray synthetic generation. Another class of detection leverages supervised machine learning: classifiers trained on labeled…