Color Palette Analysis
Saturation & Color Diversity
Analyzes color distribution including saturation levels, color diversity, and white balance consistency. AI images often have oversaturated colors.
Frequently Asked Questions
What is color palette analysis?
Examination of color distribution including saturation levels, hue diversity, and color balance across the image to detect AI generation artifacts.
Why are AI images oversaturated?
AI models are often trained on heavily edited images with boosted colors. They learn to replicate this "enhanced" look, producing colors more vibrant than typical camera output.
What is color diversity?
Measured using hue histogram entropy, this indicates how many distinct colors are present. AI images may have artificially limited or exaggerated color variety.
How is white balance checked?
By comparing color temperature (red/blue ratio) across image regions. Real scenes have consistent temperature; AI may have unnatural variations.
Can post-processing fool this?
Yes, color grading can normalize AI colors. This is why color palette has low weight - it's a supporting indicator rather than definitive proof.
What saturation levels indicate AI?
Mean saturation above 140 (on 0-255 scale) suggests AI generation. Most camera photos fall in the 40-100 range without heavy editing.
Does this work on black and white photos?
Color palette analysis is not applicable to true B&W images. Other detection methods handle grayscale content more effectively.
What about stylized/artistic photos?
Heavily stylized real photos may trigger false positives. The low 3% weight prevents this from significantly affecting overall detection accuracy.
How is saturation uniformity analyzed?
Real photos have varying saturation based on lighting and materials. AI often produces unnaturally uniform saturation across different objects in a scene.
Can this detect specific AI models?
Different AI models have different color biases. Midjourney tends toward vibrant colors, while Stable Diffusion versions vary. The detector looks for general non-natural patterns.
Verwandte Methoden
ML-Erkennung
Unsere ML-Erkennung nutzt modernste Transformer-Modelle, die auf Millionen von Bildern trainiert wurden.
PRNU-Analyse
Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.
Frequenzanalyse
Die Frequenzdomänenanalyse untersucht die Verteilung von Hoch- und Niederfrequenzkomponenten in einem Bild. KI-generierte Bilder fehlt typischerweise das natürliche Hochfrequenzrauschen echter Fotografien.
Gradientenanalyse
Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.
Rauschmuster
Echte Fotografien enthalten einzigartige Rauschmuster von Kamerasensoren, die über das Bild variieren. KI-generierte Bilder haben eine unnatürlich gleichmäßige Rauschverteilung.
Metadatenanalyse
Bildmetadaten enthalten wertvolle Hinweise auf den Ursprung. Wir analysieren EXIF-Daten, Softwaresignaturen und andere eingebettete Informationen, um KI-Generierungswerkzeuge zu identifizieren.
GAN-Fingerabdruck
Erkennt GAN-spezifische Artefakte wie Schachbrettmuster, Farbbanding und spektrale Anomalien.
Texturanalyse
Local Binary Pattern Analyse für Texturanomalien in KI-generierten Bildern.
Anatomische Erkennung
KI-Bildgeneratoren erzeugen oft anatomische Fehler, die Menschen sofort als falsch erkennen. Wir nutzen Computer Vision, um diese verräterischen Fehler zu erkennen.
C2PA-Verifizierung
C2PA (Coalition for Content Provenance and Authenticity) ist ein Industriestandard zur Verfolgung von Ursprung und Geschichte digitaler Inhalte durch kryptografische Signaturen.
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.
Chromatic Aberration Analysis
Detects the absence of chromatic aberration (color fringing) that real camera lenses produce. AI images lack these optical artifacts.
Micro-Texture Analysis
Analyzes microscopic texture patterns for repetition, uniformity, and unnatural randomness that AI generators often exhibit.
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Alle Methoden werden mit gewichteter Bewertung kombiniert, um ein endgültiges Urteil mit Konfidenzniveau zu erzeugen.
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