Edge Sharpness Analysis

Real photos have varying sharpness based on focus distance - objects at the focal plane are sharp while others are blurred. AI often produces uniformly sharp images throughout.

The detector measures sharpness at different estimated depths. Real DoF follows optical laws with smooth blur transitions; AI blur often has abrupt changes.

AI models don't simulate lens optics. They generate all details equally unless specifically prompted for bokeh, resulting in flat sharpness profiles.

Yes, AI-generated blur patterns often have unrealistic characteristics like uniform blur circles, incorrect bokeh shapes, or blur that doesn't correlate with depth.

Deep depth-of-field photos still have subtle sharpness variations and proper lens characteristics like diffraction softening. AI doesn't replicate these optical properties.

AI upscaling often adds sharpness uniformly. This creates unnaturally consistent edge profiles that differ from optically captured detail.

This measures how much sharpness varies across the image. Real photos have high variation (0.3-0.8); AI images often have low variation (0.1-0.3).

Yes, motion blur is directional while DoF blur is uniform. AI often confuses these, creating motion-style blur when DoF is intended.

Post-processing sharpening can affect results, but it creates distinctive halo artifacts that the detector also identifies as non-natural patterns.

Sharpness analysis provides useful signals but can be affected by post-processing and display scaling. It complements other methods rather than serving as a primary detector.