What Do AI Detectors Look For? Signals, Methods, and Accuracy

AI detectors assess whether text, images, video, audio, or combined media resemble patterns associated with generated or manipulated content, using signals such as word predictability, stylistic repetition, pixel and frequency artifacts, temporal inconsistencies, synthetic speech characteristics, metadata, watermarks, and provenance records. Their outputs are probabilistic rather than proof of authorship, and reliability can decline when content is short, edited, compressed, translated, cropped, re-recorded, or produced by unfamiliar generators. Research cited in the piece indicates that both human and automated detection can perform inconsistently across real-world conditions, with false positives posing particular concerns for structured, translated, or non-native writing and false negatives remaining a risk for new or altered synthetic media. The article argues that headline accuracy is insufficient without examining false-positive and false-negative rates, calibration, rejected files, representative testing, and performance across languages, platforms, and transformations. It distinguishes forensic detection from watermarking, which checks for an embedded compatible signal, and content provenance, which validates signed information about an asset’s origin and edits, while noting that layered use of these methods alongside source verification and human review is more dependable than relying on one detector score. It also discusses EU AI Act transparency obligations and presents Resemble AI’s multimodal detection, watermarking, provenance, and review-oriented tools as part of a broader fraud, identity, media-verification, and communication-security workflow.

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