Most AI detectors hide their accuracy numbers. We think that says everything about how much they trust their own product. Here’s ours.
Tested on a dataset of known AI-generated images and verified real photographs.
These numbers will change as AI models improve. We update this page when we run new tests. Last updated: June 2, 2026
Tested against: Midjourney v6, DALL-E 3, Stable Diffusion XL, Flux, real camera photographs from various devices
Three independent signals are combined into a single weighted confidence score.
Claude AI examines the image for generation artifacts, unnatural patterns, diffusion model signatures, and logical inconsistencies. Contributes 55% of the combined score.
Checks for missing camera data, suspicious software markers, and date inconsistencies. Abstains entirely if no metadata is present, with no penalty for clean images. Contributes 25% of the combined score.
Re-compresses the image and measures where pixels changed. Edited or synthetically generated regions show abnormal error patterns. Contributes 20% of the combined score.
No detector is perfect. Here’s where AIVerify is most likely to have trouble, so you can weigh results accordingly.
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