Chromatic Aberration Analysis
Missing Lens Artifacts
Detects the absence of chromatic aberration (color fringing) that real camera lenses produce. AI images lack these optical artifacts.
Frequently Asked Questions
What is chromatic aberration?
Chromatic aberration (CA) is color fringing at high-contrast edges caused by lenses bending different wavelengths of light at slightly different angles. It's visible as red/cyan or blue/yellow fringes.
Why don't AI images have CA?
AI generates images pixel by pixel without simulating optical physics. There's no virtual lens to create chromatic aberration, resulting in unnaturally clean edges.
Do modern cameras have CA?
Even expensive lenses have some CA. While in-camera software may reduce visible CA, traces remain in the raw data, especially at wide apertures or frame edges.
Can smartphone photos be detected?
Smartphones have small lenses with noticeable CA, especially at edges. However, computational photography often corrects this, reducing detection reliability.
How is CA measured?
By comparing red, green, and blue channel gradients at edges. Real CA shows consistent channel offsets; AI images have channels aligned perfectly at edges.
What about lens profile corrections?
Software corrections reduce visible CA but leave residual patterns. Complete CA removal is difficult, so traces usually remain for forensic analysis.
Is lateral and longitudinal CA detected?
Primarily lateral CA (color fringing increasing toward edges). Longitudinal CA (focus color shifts) is harder to detect but can provide additional signals.
Why is the weight only 3%?
CA correction is common in photo processing, and smartphone computational photography removes it. This makes CA absence less reliable as a standalone indicator.
Can AI add fake CA?
AI could theoretically add CA post-generation, but current models don't. Even if added, fake CA patterns would likely differ from authentic lens CA characteristics.
Does image resizing affect CA detection?
Downscaling reduces CA visibility but doesn't eliminate it. Very small images may have insufficient detail for reliable CA analysis.
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