GAN Fingerprint Detection
Detección de artefactos
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
Métodos relacionados
Detección ML
Nuestra detección ML usa modelos Transformer entrenados en millones de imágenes.
Análisis PRNU
Photo Response Non-Uniformity (PRNU) detects unique camera sensor fingerprints from manufacturing imperfections. AI images cannot replicate these authentic sensor signatures.
Análisis de Frecuencia
El análisis del dominio de frecuencia examina la distribución de componentes de alta y baja frecuencia en una imagen. Las imágenes generadas por IA típicamente carecen del ruido natural de alta frecuencia presente en fotografías reales.
Análisis de gradiente
Analyzes edge patterns and texture characteristics using Sobel, Canny, and Laplacian operators. AI images often have unnaturally smooth or uniform gradients.
Patrón de ruido
Las fotografías reales contienen patrones de ruido únicos de los sensores de cámara que varían a través de la imagen. Las imágenes generadas por IA tienen una distribución de ruido anormalmente uniforme.
Análisis de metadatos
Los metadatos de imagen contienen pistas valiosas sobre su origen. Analizamos datos EXIF, firmas de software y otra información incrustada para identificar herramientas de generación de IA.
Análisis de textura
Análisis Local Binary Pattern para anomalías de textura en imágenes IA.
Detección Anatómica
Los generadores de imágenes de IA a menudo crean errores anatómicos que los humanos reconocen inmediatamente como incorrectos. Usamos visión por computadora para detectar estos errores reveladores.
Verificación C2PA
C2PA (Coalition for Content Provenance and Authenticity) es un estándar de la industria para rastrear el origen y la historia del contenido digital a través de firmas criptográficas.
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.
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