Gradient Analysis
Edge & Texture Detection
Gradient analysis examines edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. Real photos have natural gradient distributions from optical effects, while AI images often have unnaturally smooth or uniform edge patterns.
How Gradient Analysis Works
Real cameras introduce specific gradient patterns from optical effects (lens distortion, chromatic aberration), sensor characteristics, and demosaicing algorithms. AI-generated images lack these physical constraints, producing either too-smooth or artificially sharp edges.
Sobel
Edge gradients X/Y
Canny
Edge density
Laplacian
Focus variance
Entropy
Distribution analysis
Key Metrics
Gradient Entropy
Measures the randomness of gradient magnitude distribution. AI images often have lower entropy due to uniform textures.
Edge Density
Ratio of edge pixels to total pixels. AI images tend to have fewer natural edge details compared to real photos.
Laplacian Variance
Measures sharpness variation across the image. Real photos have natural focus falloff, while AI can be unnaturally uniform.
Direction Uniformity
Analyzes gradient direction distribution. AI often produces unnaturally uniform gradient directions.
Why Gradient Analysis Works
- ● Physical Constraints: Real cameras have optical imperfections that create unique edge patterns
- ● Demosaicing Artifacts: RAW-to-RGB conversion leaves detectable patterns in real photos
- ● Fast Processing: Gradient operations are computationally efficient
- ● Complementary: Works well combined with other detection methods
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.
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.
Análisis de textura
Análisis Local Binary Pattern para anomalías de textura en imágenes IA.
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