# Identification of AI-Generated Rock Thin-Section Images by Feature Analysis Under Data Scarcity
## 2 Materials and Methods
### Feature Extraction and Selection  
A range of global and local parameters was calculated from the preprocessed images. In total, 70 parameters were computed, consisting of 33 object-based descriptors and 37 global texture descriptors. The texture parameters were calculated based on the main thematic areas indicated in Table 1, including colour, texture, and spectral characteristics. For the segmented images, geometric features were extracted for objects representing color clusters (interpreted as grains). The features of each detected object were computed: area, eccentricity, solidity, extent, and perimeter. As the number of objects varied between images, these features were consolidated into a standardized global feature vector. This was achieved by computing statistical measures for each feature: mean, standard deviation, minimum, maximum, skewness, and kurtosis, resulting in a total of 30 descriptors. Addi \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Description** \\ \hline
1. Global Entropy & Image-wide entropy (computed on an 8-bit image)—a measure of brightness disorder. \\
2. Histogram Entropy & Luminance histogram entropy—analysis of the distribution of grayscale levels. \\
3. Benford Deviation & Deviation from [PERSON]’s law for the first digits of pixel values—a statistical anomaly indicator. \\
4. Colorfulness & Measure of color saturation and diversity—based on the color axis derived from opponent color channels. \\
5. Hue Histogram Peaks & Number of peaks in the hue histogram (H in HSV)—color complexity. \\
6. Mean Local Entropy & Mean of local entropies measured in \(10\times 10\) windows—local information variability. \\
7. Z-score Deviation & Local homogeneity, estimated via the standard deviation of Z-scores across \(5\times 5\) pixel neighborhoods. \\
8, 9. Noise Estimate & Noise estimation based on the mean standard deviation of pixel intensity differences in the vertical and horizontal directions, normalized to the global image contrast. \\
10. LBP Mean & Mean value of the Local Binary Pattern (LBP) histogram. \\
11. DFT CenterToEdgeRatio & Ratio of energy in the center of the DFT spectrum to peripheral energy. \\
12. DFT Entropy & Entropy of the frequency spectrum (DFT) as an energy dispersion. \\
13. DFT Skewness & Skewness of the frequency spectrum as a frequency asymmetry. \\
14. Wavelet ApprovVar & Variance of approximation coefficients (low-frequency) from the Daubechies-1 (db1) wavelet. \\
15-17. Wavelet Horiz., Vert., Diag. & Variance of horizontal, vertical, and diagonal detail coefficients. \\
18–21. Norm Wavelet Horiz., Vert., Diag. & Normalized versions of wavelet variances (relative to global variance). \\
22, 23. Laplacian Variance & Laplacian filter variability—edge and detail detection; normalized as Laplacian variance divided by the global image variance. \\
24. Histogram Peak Count & Number of local maxima in the histogram—image tone structure. \\
25. GLCM Contrast & Pixel co-occurrence indicators. Mean GLCM contrast from 4 directions—local gray-level variation. \\
26. GLCM Correlation & Mean GLCM correlation—relationship between neighboring pixels. \\
27. GLCM Energy & Mean of GLCM Energy, texture uniformity. \\
28. GLCM Homogeneity & Mean of GLCM Homogeneity, higher values indicate more homogeneous textures. \\  & As above (contrast, correlation, energy, homogeneity), but computed on the gradient map (GDLCM), offering increased sensitivity to edges and fine details. \\
29-32. GDLCM & Mean values of standard Tamura parameters: coarseness (image structure granularity), contrast \\
30-35. Tamura & (brightness complexity), and directionality (number of dominant gradient directions). \\
36. Norm Colorfulness & Colorfulness normalized to mean brightness. \\
37. Saturation Deviation & Standard deviation of the S component in HSV—saturation variability. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A grouped list of selected global feature categories, along with a description of the calculation method.