9% Weight - Physics Analysis

Lighting Physics Validation

Shadow & Color Temperature

Validates light source consistency, shadow direction physics, specular highlight accuracy, and color temperature uniformity across the image.

82-90%
Accuracy
9%
Weight
Lighting Physics Validation

So funktioniert's

💡

Light Sources

Detect all lights

🌑

Shadows

Physics validation

🌡️

Color Temp

Consistency check

🔲

Occlusion

Ambient analysis

Frequently Asked Questions

What is light source consistency?

This checks if all highlights and specular reflections point to the same light source(s). AI often creates highlights that suggest conflicting light positions.

How is shadow physics validated?

The detector analyzes shadow edges, penumbra gradients, and contact shadows. Real shadows obey inverse-square law falloff; AI shadows often have unrealistic hard edges or wrong intensity gradients.

What is color temperature analysis?

Real scenes have consistent color temperature from light sources. AI images often mix warm and cool lighting inconsistently, creating unnatural white balance variations.

What is ambient occlusion detection?

Ambient occlusion is the darkening in corners and crevices where light is blocked. AI often forgets to add natural AO or applies it inconsistently.

Does this work on night photos?

Yes, night photos often have multiple artificial light sources. AI frequently creates impossible lighting in night scenes, making them easier to detect.

Can professional lighting fool this detector?

Complex studio lighting creates multiple light sources, but they still obey physics. Real multi-light setups are consistent; AI multi-light attempts often have contradictory physics.

How accurate is light direction estimation?

By analyzing highlight positions on known 3D shapes (spheres, faces), light direction can be estimated within 15-20 degrees for typical images.

What about HDR or stylized photos?

HDR processing can change lighting appearance but maintains physical consistency. Stylized photos may trigger false positives, which is why this method has 9% weight in ensemble.

How does this improve overall detection?

Lighting validation catches errors that pattern-based detectors miss. AI can match statistical patterns while still violating physics, making this a valuable complementary method.

Will future AI fix lighting errors?

Some AI systems are incorporating physics-based rendering knowledge, which may reduce lighting errors. However, complete physical accuracy requires 3D understanding that current AI lacks.

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.

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.

Color Palette Analysis

Analyzes color distribution including saturation levels, color diversity, and white balance consistency. AI images often have oversaturated colors.

Ihr Bild Prüfen

Alle Methoden werden mit gewichteter Bewertung kombiniert, um ein endgültiges Urteil mit Konfidenzniveau zu erzeugen.

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