AI generation is probabilistic, not binary. Because the model samples from a distribution rather than applying a fixed rule, no single visual or technical sign can prove that an image or video was AI-generated.
Why there is no universal tell
A generative model learns patterns from training data and then samples new output. Sampling means variation: the same prompt can produce a flawless result or a flawed one. A clue such as malformed fingers is therefore a tendency in the output distribution, not a property of the process. It raises the probability that AI was involved; it does not establish it.
A real photo that looks generated
Consider a genuine portrait shot in dim light with a phone camera. Aggressive noise reduction smooths the skin until pores disappear, and the shallow depth of field blurs the ear into the background. Both traits — unnaturally smooth skin and a soft, merging edge — are things people often list as AI signs. The photo is real. If you treated either trait as proof, you would be wrong about a real person.
Why false positives deserve attention
A false positive is real media labeled as AI. It can discredit authentic evidence, accuse a photographer of fabrication, or dismiss a genuine event. Because the cost falls on real people and real records, an honest detector treats a single clue as a reason to look closer, not as a verdict.