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.
Gerelateerde methoden
ML Detection
Our machine learning detection uses state-of-the-art transformer models trained on millions of images to distinguish between authentic photographs and AI-generated content.
PRNU Analysis
Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.
Frequentieanalyse
Frequency domain analysis examines the distribution of high and low frequency components in an image. AI-generated images typically lack the natural high-frequency noise present in real photographs.
Gradient Analysis
Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.
Noise Pattern
Real photographs contain unique noise patterns from camera sensors that vary across the image. AI-generated images have unnaturally uniform noise distribution.
Metadata Analysis
Image metadata contains valuable clues about its origin. We analyze EXIF data, software signatures, and other embedded information to identify AI generation tools.
GAN Fingerprint
Detects GAN-specific artifacts like checkerboard patterns, color banding, and spectral anomalies unique to generative adversarial networks.
Texture Analysis
Local Binary Pattern analysis for texture anomalies common in AI-generated images. Measures uniformity, entropy, and homogeneity.
Anatomy Detection
AI image generators often create anatomical errors that humans immediately recognize as wrong. We use computer vision to detect these telltale mistakes.
C2PA Verification
C2PA (Coalition for Content Provenance and Authenticity) is an industry standard for tracking the origin and history of digital content through cryptographic signatures.
Semantic Inconsistency Detection
Detects logical inconsistencies like incorrect shadows, impossible perspectives, distorted reflections, and violations of physical laws that AI often produces.
Human Biometric Analysis
Uses MediaPipe to analyze human anatomy for incorrect finger counts, asymmetric eyes, unnatural skin texture, and other anatomical anomalies common in AI-generated faces.
Lighting Physics Validation
Validates light source consistency, shadow direction physics, specular highlight accuracy, and color temperature uniformity across the image.
Compression Artifact Analysis
Analyzes JPEG compression artifacts to estimate quality levels and detect re-compression patterns that indicate image manipulation or AI generation.
Edge Sharpness Analysis
Analyzes sharpness distribution across the image and validates depth-of-field consistency. AI often produces unnaturally uniform sharpness.
Statistical Pattern Analysis
Analyzes statistical properties including Shannon entropy, histogram patterns, and Benford's Law compliance to detect synthetic image characteristics.
Micro-Texture Analysis
Analyzes microscopic texture patterns for repetition, uniformity, and unnatural randomness that AI generators often exhibit.
Color Palette Analysis
Analyzes color distribution including saturation levels, color diversity, and white balance consistency. AI images often have oversaturated colors.
Controleer afbeelding
All methods are combined using weighted scoring to produce a final verdict with confidence level.
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