GAN Fingerprint Detection
アーティファクト検出
Generative Adversarial Networks (GANs) leave distinctive fingerprints in the images they create. Our GAN Fingerprint detector analyzes multiple characteristics that are typical of GAN-generated content.
How It Works
These fingerprints arise from the mathematical operations used in GAN architectures, particularly upsampling layers and the generator network architecture.
Detection Methods
Checkerboard Pattern Detection
Transposed convolution layers in GANs often create checkerboard artifacts - periodic patterns visible in the frequency domain. We detect these using FFT analysis.
Color Banding Analysis
GANs often produce subtle color bands in smooth gradients due to limited color precision. We analyze gradient regions for unnatural color transitions.
Spectral Anomaly Detection
GAN images show unusual peaks in their frequency spectrum. We analyze the DCT spectrum for distinctive GAN signatures that differ from natural images.
Upsampling Artifact Detection
GAN generators use upsampling to increase image resolution. This process creates periodic artifacts that we detect using autocorrelation analysis.
Technical Details
GAN Artifacts We Detect
- Checkerboard patterns from transposed convolution
- Periodic patterns at specific frequencies
- Color quantization in smooth gradients
- Unnatural spectral energy distribution
GAN Types Detected
- StyleGAN / StyleGAN2 / StyleGAN3
- ProGAN / BigGAN
- DCGAN variants
- CycleGAN / Pix2Pix
関連方法
ML検出
数百万の画像で訓練された最先端のTransformerモデルを使用して、本物の写真とAI生成コンテンツを区別します。
PRNU分析
Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.
周波数分析
DCT（離散コサイン変換）を用いて画像の高周波・低周波成分の分布を分析。AI生成画像はカメラで撮影された写真に存在する自然な高周波ノイズが欠如しており、この特徴で真偽を判定します。無料オンラインツール。
勾配分析
Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.
ノイズパターン
Real photographs contain unique noise patterns from camera sensors that vary across the image. AI-generated images have unnaturally uniform noise distribution.
メタデータ分析
Image metadata contains valuable clues about its origin. We analyze EXIF data, software signatures, and other embedded information to identify AI generation tools.
テクスチャ分析
AI生成画像に見られるテクスチャ異常のLocal Binary Pattern分析。均一性、エントロピー、均質性を測定。
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.
画像をチェック
All methods are combined using weighted scoring to produce a final verdict with confidence level.
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