Lighting Physics Validation
Shadow & Color Temperature
Validates light source consistency, shadow direction physics, specular highlight accuracy, and color temperature uniformity across the image.
工作原理
Light Sources
Detect all lights
Shadows
Physics validation
Color Temp
Consistency check
Occlusion
Ambient analysis
Frequently Asked Questions
What is light source consistency?
This checks if all highlights and specular reflections point to the same light source(s). AI often creates highlights that suggest conflicting light positions.
How is shadow physics validated?
The detector analyzes shadow edges, penumbra gradients, and contact shadows. Real shadows obey inverse-square law falloff; AI shadows often have unrealistic hard edges or wrong intensity gradients.
What is color temperature analysis?
Real scenes have consistent color temperature from light sources. AI images often mix warm and cool lighting inconsistently, creating unnatural white balance variations.
What is ambient occlusion detection?
Ambient occlusion is the darkening in corners and crevices where light is blocked. AI often forgets to add natural AO or applies it inconsistently.
Does this work on night photos?
Yes, night photos often have multiple artificial light sources. AI frequently creates impossible lighting in night scenes, making them easier to detect.
Can professional lighting fool this detector?
Complex studio lighting creates multiple light sources, but they still obey physics. Real multi-light setups are consistent; AI multi-light attempts often have contradictory physics.
How accurate is light direction estimation?
By analyzing highlight positions on known 3D shapes (spheres, faces), light direction can be estimated within 15-20 degrees for typical images.
What about HDR or stylized photos?
HDR processing can change lighting appearance but maintains physical consistency. Stylized photos may trigger false positives, which is why this method has 9% weight in ensemble.
How does this improve overall detection?
Lighting validation catches errors that pattern-based detectors miss. AI can match statistical patterns while still violating physics, making this a valuable complementary method.
Will future AI fix lighting errors?
Some AI systems are incorporating physics-based rendering knowledge, which may reduce lighting errors. However, complete physical accuracy requires 3D understanding that current AI lacks.
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。
纹理分析
局部二值模式分析用于检测AI生成图像中常见的纹理异常。测量均匀性、熵和同质性。
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.
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.