Compression Artifact Analysis
JPEG Forensics
Analyzes JPEG compression artifacts to estimate quality levels and detect re-compression patterns that indicate image manipulation or AI generation.
Frequently Asked Questions
How is JPEG quality estimated?
By analyzing DCT coefficients in 8x8 blocks and comparing quantization patterns. Different JPEG quality settings produce distinctive coefficient distributions.
What are blocking artifacts?
JPEG compresses in 8x8 pixel blocks. Heavy compression creates visible block boundaries. AI output often lacks these natural JPEG artifacts or has unusual patterns.
Can you detect double compression?
Yes, re-saving a JPEG creates distinctive dual patterns in DCT histograms. This helps identify manipulated images that were saved multiple times.
Why do AI images have different compression patterns?
AI generates images at the pixel level without camera-like compression. When saved as JPEG, the compression patterns differ from camera-originated images.
Does PNG format bypass this detection?
PNG uses lossless compression, so this specific method doesn't apply. However, other detection methods work on PNG files effectively.
What is quantization noise analysis?
JPEG quantization introduces specific noise patterns. Camera sensors add their own noise on top. AI images lack this layered noise characteristic.
How does multiple re-saves affect detection?
Each re-save adds compression artifacts. Real photos typically show 1-2 compression generations; AI shared on social media may show many more.
What JPEG quality range is most informative?
Quality 70-90% provides the best balance. Higher quality has fewer artifacts to analyze; lower quality obscures original characteristics with heavy compression.
Is this effective against all AI models?
Yes, regardless of the AI model used, the output format determines compression patterns. All AI images show non-camera-like compression when saved as JPEG.
Why is the weight only 7%?
Compression patterns can be altered by format conversion and social media processing. This makes them less reliable than content-based methods, hence the lower weight.
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.
GANフィンガープリント
GAN（敵対的生成ネットワーク）が生成する画像のチェッカーボードパターン、カラーバンディング、スペクトル異常などの固有アーティファクトを高精度で検出。StyleGAN、ProGAN、CycleGAN対応の無料オンライン分析ツール。
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.
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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