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
관련 방법
ML 탐지
수백만 이미지로 훈련된 최첨단 Transformer 모델을 사용하여 실제 사진과 AI 생성 콘텐츠를 구별합니다.
PRNU 분석
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.
노이즈 패턴
Real photographs contain unique noise patterns from camera sensors that vary across the image. AI-generated images have unnaturally uniform noise distribution.
메타데이터 분석
Image metadata contains valuable clues about its origin. We analyze EXIF data, software signatures, and other embedded information to identify AI generation tools.
GAN 지문
체커보드 패턴, 컬러 밴딩, 생성적 적대 신경망 고유의 스펙트럼 이상과 같은 GAN 특유 아티팩트를 탐지.
텍스처 분석
AI 생성 이미지에서 흔히 발견되는 텍스처 이상에 대한 Local Binary Pattern 분석.
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.
이미지 확인
All methods are combined using weighted scoring to produce a final verdict with confidence level.
지금 시도