Texture Analysis
LBP Pattern Detection
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)
Metode Terkait
ML Detection
Our machine learning detection uses state-of-the-art transformer models trained on millions of images to distinguish between authentic photographs and AI-generated content.
PRNU Analysis
Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.
Frequency Analysis
Frequency domain analysis examines the distribution of high and low frequency components in an image. AI-generated images typically lack the natural high-frequency noise present in real photographs.
Gradient Analysis
Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.
Noise Pattern
Real photographs contain unique noise patterns from camera sensors that vary across the image. AI-generated images have unnaturally uniform noise distribution.
Metadata Analysis
Image metadata contains valuable clues about its origin. We analyze EXIF data, software signatures, and other embedded information to identify AI generation tools.
GAN Fingerprint
Detects GAN-specific artifacts like checkerboard patterns, color banding, and spectral anomalies unique to generative adversarial networks.
Anatomy Detection
AI image generators often create anatomical errors that humans immediately recognize as wrong. We use computer vision to detect these telltale mistakes.
C2PA Verification
C2PA (Coalition for Content Provenance and Authenticity) is an industry standard for tracking the origin and history of digital content through cryptographic signatures.
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.
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All methods are combined using weighted scoring to produce a final verdict with confidence level.
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