Texture Analysis
LBP模式检测
Local Binary Patterns (LBP) is a powerful texture descriptor that captures micro-patterns in images. AI-generated images often have different texture characteristics compared to real photographs.
Local Binary Patterns (LBP)
How LBP Works
LBP compares each pixel with its surrounding neighbors. If a neighbor is greater than or equal to the center pixel, it gets a 1; otherwise, it gets a 0. The resulting binary pattern encodes local texture information.
This creates a histogram of texture patterns that is rotation-invariant and robust to illumination changes.
5 6 8
4 [6] 9 → Binary: 01110001 → Decimal: 113
2 7 3Example: Center pixel = 6, neighbors compared clockwise
Texture Metrics
LBP Uniformity
Measures how uniform the LBP histogram is. AI images often show abnormally high uniformity due to synthetic texture generation.
LBP Entropy
Calculates the information entropy of the LBP histogram. Lower entropy indicates less texture variety, common in AI-generated content.
Texture Contrast
Measures local contrast in the texture. AI images tend to have different contrast patterns compared to real photos captured by cameras.
Texture Homogeneity
Evaluates the smoothness of texture transitions. AI generators often produce unnaturally smooth or overly uniform texture areas.
Technical Details
LBP Parameters
- • Radius: 1 pixel (default)
- • Neighbors: 8 sampling points
- • Histogram bins: 256
- • Gray-level co-occurrence matrix (GLCM)
AI Indicators
- High LBP uniformity (> 0.35)
- Low entropy (< 4.5)
- Abnormal contrast distribution
- Excessive homogeneity (> 0.8)
相关方法
机器学习检测
我们的机器学习检测使用最先进的Transformer模型，在数百万张图像上训练，以区分真实照片和AI生成内容。
PRNU分析
光响应非均匀性(PRNU)检测来自制造缺陷的独特相机传感器指纹。AI图像无法复制这些真实的传感器签名。
频率分析
频域分析检查图像中高频和低频分量的分布。AI生成的图像通常缺乏真实照片中存在的自然高频噪声。
梯度分析
使用Sobel、Canny和Laplacian算子分析边缘模式和纹理特征。AI图像通常具有不自然平滑或均匀的梯度。
噪声模式
真实照片包含来自相机传感器的独特噪声模式，这些模式在图像中有所不同。AI生成的图像具有不自然的均匀噪声分布。
元数据分析
图像元数据包含关于其来源的重要线索。我们分析EXIF数据、软件签名和其他嵌入信息以识别AI生成工具。
GAN指纹
检测GAN特有的伪影，如棋盘格图案、色带和生成对抗网络特有的频谱异常。
人体解剖检测
AI图像生成器经常创建人类立即识别为错误的解剖错误。我们使用计算机视觉来检测这些明显的错误。
C2PA验证
C2PA是通过加密签名跟踪数字内容来源和历史的行业标准。
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.