Texture Analysis
Detección de patrones 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.
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 3Example: 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)
Métodos relacionados
Detección ML
Nuestra detección ML usa modelos Transformer entrenados en millones de imágenes.
Análisis PRNU
Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.
Análisis de Frecuencia
El análisis del dominio de frecuencia examina la distribución de componentes de alta y baja frecuencia en una imagen. Las imágenes generadas por IA típicamente carecen del ruido natural de alta frecuencia presente en fotografías reales.
Análisis de gradiente
Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.
Patrón de ruido
Las fotografías reales contienen patrones de ruido únicos de los sensores de cámara que varían a través de la imagen. Las imágenes generadas por IA tienen una distribución de ruido anormalmente uniforme.
Análisis de metadatos
Los metadatos de imagen contienen pistas valiosas sobre su origen. Analizamos datos EXIF, firmas de software y otra información incrustada para identificar herramientas de generación de IA.
Huella GAN
Detecta artefactos específicos de GAN como patrones de tablero de ajedrez y bandas de color.
Detección Anatómica
Los generadores de imágenes de IA a menudo crean errores anatómicos que los humanos reconocen inmediatamente como incorrectos. Usamos visión por computadora para detectar estos errores reveladores.
Verificación C2PA
C2PA (Coalition for Content Provenance and Authenticity) es un estándar de la industria para rastrear el origen y la historia del contenido digital a través de firmas criptográficas.
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.
Verificar Tu Imagen
Todos los métodos se combinan usando puntuación ponderada para producir un veredicto final con nivel de confianza.
Probar Ahora