AI-generated content detection can use intentional watermarks, model-specific patterns, metadata, content credentials, or combined evidence. Each method has limits based on model support, content length, transformations, thresholds, and how the material was created.
Removal tools turn detection into an adversarial contest. Consequential decisions should account for false positives and false negatives and should not treat one detector result as complete proof of authorship or deceptive intent.
ELI5
AI-generated content detection estimates whether text, images, audio, video or code may have been created or heavily changed by AI. It can look for statistical patterns, provenance records, watermarks or metadata.
For example, a detector might report that an essay resembles model-generated text, but editing, translation and different writing styles can change the result. Because detectors can be wrong, their score should not be treated as proof of misconduct without other evidence, transparent rules and a way to appeal.
