Color Palette Analysis
Saturation & Color Diversity
Analyzes color distribution including saturation levels, color diversity, and white balance consistency. AI images often have oversaturated colors.
Frequently Asked Questions
What is color palette analysis?
Examination of color distribution including saturation levels, hue diversity, and color balance across the image to detect AI generation artifacts.
Why are AI images oversaturated?
AI models are often trained on heavily edited images with boosted colors. They learn to replicate this "enhanced" look, producing colors more vibrant than typical camera output.
What is color diversity?
Measured using hue histogram entropy, this indicates how many distinct colors are present. AI images may have artificially limited or exaggerated color variety.
How is white balance checked?
By comparing color temperature (red/blue ratio) across image regions. Real scenes have consistent temperature; AI may have unnatural variations.
Can post-processing fool this?
Yes, color grading can normalize AI colors. This is why color palette has low weight - it's a supporting indicator rather than definitive proof.
What saturation levels indicate AI?
Mean saturation above 140 (on 0-255 scale) suggests AI generation. Most camera photos fall in the 40-100 range without heavy editing.
Does this work on black and white photos?
Color palette analysis is not applicable to true B&W images. Other detection methods handle grayscale content more effectively.
What about stylized/artistic photos?
Heavily stylized real photos may trigger false positives. The low 3% weight prevents this from significantly affecting overall detection accuracy.
How is saturation uniformity analyzed?
Real photos have varying saturation based on lighting and materials. AI often produces unnaturally uniform saturation across different objects in a scene.
Can this detect specific AI models?
Different AI models have different color biases. Midjourney tends toward vibrant colors, while Stable Diffusion versions vary. The detector looks for general non-natural patterns.
İlgili Yöntemler
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.
Frekans Analizi
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.
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.
Resmi kontrol et
All methods are combined using weighted scoring to produce a final verdict with confidence level.
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