Statistical Pattern Analysis
Entropy & Benford's Law
Analyzes statistical properties including Shannon entropy, histogram patterns, and Benford's Law compliance to detect synthetic image characteristics.
Frequently Asked Questions
What is image entropy?
Entropy measures information content based on pixel value distribution. Natural images have specific entropy ranges; AI images often have abnormally high or low entropy.
How does Benford's Law apply to images?
Benford's Law describes the expected frequency of leading digits in natural data. Real DCT coefficients follow this distribution; AI-generated images often deviate from it.
What is histogram analysis?
Analyzing the distribution of pixel values. Real photos have smooth histograms shaped by scene content; AI images may have gaps, spikes, or unusual symmetry.
What is spatial autocorrelation?
This measures how pixel values correlate with neighbors. Real images from sensors have specific correlation patterns; AI generation creates different spatial relationships.
Why are statistics different for AI?
AI generates images through mathematical processes that create detectable patterns. Even when visually perfect, statistical fingerprints remain different from camera output.
Can editing change statistics enough?
Editing affects statistics, which is why this method has lower weight. Heavy editing can normalize AI statistics, but typically introduces other detectable artifacts.
What are first-digit frequencies?
Benford's Law predicts ~30% of first digits should be "1", ~17% "2", etc. AI DCT coefficients often show flatter distributions not matching these natural ratios.
Is this method standalone effective?
Statistical analysis alone has 70-80% accuracy. It works best combined with other methods, providing additional signal especially when visual analysis is uncertain.
What image regions are analyzed?
Both global statistics and local patch statistics are computed. AI images often show suspicious uniformity of statistics across patches that would naturally vary.
Does resolution affect accuracy?
Higher resolution provides more data for reliable statistics. Very small images may not have enough samples for accurate Benford analysis; minimum 256x256 is recommended.
Related Methods
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.
Texture Analysis
Local Binary Pattern analysis for texture anomalies common in AI-generated images. Measures uniformity, entropy, and homogeneity.
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.
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.
Check Your Image
All methods are combined using weighted scoring to produce a final verdict with confidence level.
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