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

Comment ça marche

💡

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.

Méthodes associées

Détection ML

Notre détection ML utilise des modèles Transformer entraînés sur des millions d'images.

Analyse PRNU

Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.

Analyse Fréquentielle

L'analyse du domaine fréquentiel examine la distribution des composantes haute et basse fréquence d'une image. Les images générées par IA manquent généralement du bruit naturel haute fréquence présent dans les vraies photographies.

Analyse de gradient

Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.

Motif de bruit

Les vraies photographies contiennent des motifs de bruit uniques des capteurs de caméra qui varient à travers l'image. Les images générées par IA ont une distribution de bruit anormalement uniforme.

Analyse des métadonnées

Les métadonnées d'image contiennent des indices précieux sur son origine. Nous analysons les données EXIF, les signatures logicielles et autres informations intégrées pour identifier les outils de génération IA.

Empreinte GAN

Détecte les artefacts spécifiques aux GAN comme les motifs en damier et le banding couleur.

Analyse de texture

Analyse Local Binary Pattern pour les anomalies de texture dans les images IA.

Détection Anatomique

Les générateurs d'images IA créent souvent des erreurs anatomiques que les humains reconnaissent immédiatement comme fausses. Nous utilisons la vision par ordinateur pour détecter ces erreurs révélatrices.

Vérification C2PA

C2PA (Coalition for Content Provenance and Authenticity) est un standard industriel pour suivre l'origine et l'historique du contenu numérique via des signatures cryptographiques.

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.

Vérifier Votre Image

Toutes les méthodes sont combinées en utilisant un score pondéré pour produire un verdict final avec niveau de confiance.

Essayer Maintenant