9% Weight - Physics Analysis

Lighting Physics Validation

Shadow & Color Temperature

Validates light source consistency, shadow direction physics, specular highlight accuracy, and color temperature uniformity across the image.

82-90%
Accuracy
9%
Weight
Lighting Physics Validation

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💡

Light Sources

Detect all lights

🌑

Shadows

Physics validation

🌡️

Color Temp

Consistency check

🔲

Occlusion

Ambient analysis

Frequently Asked Questions

What is light source consistency?

This checks if all highlights and specular reflections point to the same light source(s). AI often creates highlights that suggest conflicting light positions.

How is shadow physics validated?

The detector analyzes shadow edges, penumbra gradients, and contact shadows. Real shadows obey inverse-square law falloff; AI shadows often have unrealistic hard edges or wrong intensity gradients.

What is color temperature analysis?

Real scenes have consistent color temperature from light sources. AI images often mix warm and cool lighting inconsistently, creating unnatural white balance variations.

What is ambient occlusion detection?

Ambient occlusion is the darkening in corners and crevices where light is blocked. AI often forgets to add natural AO or applies it inconsistently.

Does this work on night photos?

Yes, night photos often have multiple artificial light sources. AI frequently creates impossible lighting in night scenes, making them easier to detect.

Can professional lighting fool this detector?

Complex studio lighting creates multiple light sources, but they still obey physics. Real multi-light setups are consistent; AI multi-light attempts often have contradictory physics.

How accurate is light direction estimation?

By analyzing highlight positions on known 3D shapes (spheres, faces), light direction can be estimated within 15-20 degrees for typical images.

What about HDR or stylized photos?

HDR processing can change lighting appearance but maintains physical consistency. Stylized photos may trigger false positives, which is why this method has 9% weight in ensemble.

How does this improve overall detection?

Lighting validation catches errors that pattern-based detectors miss. AI can match statistical patterns while still violating physics, making this a valuable complementary method.

Will future AI fix lighting errors?

Some AI systems are incorporating physics-based rendering knowledge, which may reduce lighting errors. However, complete physical accuracy requires 3D understanding that current AI lacks.

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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.

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.

ตรวจสอบภาพ

All methods are combined using weighted scoring to produce a final verdict with confidence level.

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