12% Weight - LBP Analysis

Texture Analysis

Détection de motifs LBP

Local Binary Patterns (LBP) is a powerful texture descriptor that captures micro-patterns in images. AI-generated images often have different texture characteristics compared to real photographs.

78-85%
Detection Accuracy
12%
Ensemble Weight
Texture Analysis

Local Binary Patterns (LBP)

How LBP Works

LBP compares each pixel with its surrounding neighbors. If a neighbor is greater than or equal to the center pixel, it gets a 1; otherwise, it gets a 0. The resulting binary pattern encodes local texture information.

This creates a histogram of texture patterns that is rotation-invariant and robust to illumination changes. 

  5   6   8
  4  [6]  9    → Binary: 01110001 → Decimal: 113
  2   7   3

Example: Center pixel = 6, neighbors compared clockwise

Texture Metrics

LBP Uniformity

Measures how uniform the LBP histogram is. AI images often show abnormally high uniformity due to synthetic texture generation.

LBP Entropy

Calculates the information entropy of the LBP histogram. Lower entropy indicates less texture variety, common in AI-generated content.

Texture Contrast

Measures local contrast in the texture. AI images tend to have different contrast patterns compared to real photos captured by cameras.

Texture Homogeneity

Evaluates the smoothness of texture transitions. AI generators often produce unnaturally smooth or overly uniform texture areas.

Technical Details

LBP Parameters

  • • Radius: 1 pixel (default)
  • • Neighbors: 8 sampling points
  • • Histogram bins: 256
  • • Gray-level co-occurrence matrix (GLCM)

AI Indicators

  • High LBP uniformity (> 0.35)
  • Low entropy (< 4.5)
  • Abnormal contrast distribution
  • Excessive homogeneity (> 0.8)

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.

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.

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.

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.

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