How to Check If an Image Is AI-Generated

Hands and small anatomical details (teeth, ears, fingers) are still a common weak point for generative models. Text rendered inside an image — signage, labels, book spines — is another: generated text is often garbled, misspelled, or nonsensical up close. Lighting and shadow direction that doesn't match across the frame, and backgrounds that blur or repeat oddly, are worth a second look too.

The newest generation models have largely fixed the most obvious tells — hands, in particular, are far less reliable as a signal than they were even a year ago. Relying on eyeballing an image is increasingly a coin flip, which is exactly why automated detection looks at signals invisible to the naked eye.

A deep-learning classifier is trained specifically to recognize the statistical artifacts generation models leave behind in pixel data — patterns a human eye can't consciously pick up on. A separate deepfake/face-manipulation check targets a different problem: photos where a real image has been face-swapped or synthetically altered rather than fully generated from scratch.

Combining both into one score, with a visible breakdown of which signal drove the result, is more transparent — and more accurate — than any single check alone. Try it on the AI Image Detector to see the breakdown on your own upload.

Heavily compressed or resized images can lose the metadata and fine pixel detail that detection relies on, which can reduce confidence. As with text and video, treat the result as a strong signal to guide further judgment, not an unappealable verdict.