周波数分析
DCTスペクトル分析
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。
How It Works
Using Discrete Cosine Transform (DCT), we decompose the image into its frequency components. Real photographs contain rich high-frequency detail from camera sensors, while AI images tend to be smoother with less high-frequency energy.
Real Photos
- • Rich high-frequency content
- • Natural camera sensor noise
- • Sharp edges and details
- • High_freq_ratio > 0.20
AI Generated
- • Smooth, low-frequency dominant
- • Uniform synthetic patterns
- • Blurry fine details
- • High_freq_ratio < 0.15
Technical Implementation
DCT (Discrete Cosine Transform)
We decompose images into frequency components using 2D DCT, then analyze the energy distribution between high and low frequency regions.
Frequently Asked Questions
What is frequency analysis for AI detection?
Frequency analysis uses DCT (Discrete Cosine Transform) to decompose images into frequency components. AI images typically lack the high-frequency detail found in real photographs.
Why do AI images lack high frequencies?
AI diffusion models generate images at lower resolutions and upsample them. This process smooths out fine details, reducing high-frequency content compared to sensor-captured photos.
What is the high-frequency ratio threshold?
Real photos typically have a high-frequency ratio above 0.20. AI-generated images often fall below 0.15, indicating their synthetic smooth nature.
How is the DCT applied?
We convert images to grayscale, apply 2D DCT, and separate the spectrum into low and high frequency regions. The ratio of energy in these regions indicates image origin.
Does image sharpening affect results?
Artificial sharpening can boost high frequencies, but the pattern differs from natural sensor noise. Our analysis considers both ratio and distribution characteristics.
Why is frequency analysis weighted at 20%?
Frequency analysis is highly reliable for detecting diffusion models but can be affected by image filters. The 20% weight reflects its strong but not absolute discriminative power.
What's the difference from FFT analysis?
DCT produces only real values (no complex numbers) and has better energy compaction for image signals. It's also what JPEG compression uses, making it well-suited for forensics.
Can upscaled AI images fool this?
AI upscalers add synthetic high-frequency detail, but these patterns are uniform and lack the natural variation of camera-captured fine detail. Detection remains effective.
What image size is optimal?
We process images at 512x512 maximum for analysis. Higher resolution provides more frequency data, but the ratios remain consistent across sizes.
Does this work on all AI generators?
Frequency analysis is particularly effective against diffusion models (DALL-E, Midjourney, Stable Diffusion). Older GAN-based generators show different but still detectable patterns.
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.
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.
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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