# Identification of AI-Generated Rock Thin-Section Images by Feature Analysis Under Data Scarcity
## 3 Results and Discussion  
The study examined 160 images across four geological groups, with 20 real and 20 synthetic images for each group. A total of 70 parameters were tested, encompassing texture feature analysis, frequency-domain characteristics, and object-based features. Tests \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Description** \\ \hline Random Forest (Tree Bagger) & A model consisting of 100 decision trees with Out-of-Bag prediction. It performs well on data with complex structures and enables analysis of feature importance. \\ Deep decision tree & A classical decision tree with the maximum number of splits equal to the number of training samples. It is characterized by high sensitivity but is prone to overfitting. \\ SVM & Support Vector Machine with a non-linear RBF kernel, automatic scaling, and a low BoxConstraint parameter (0.01), increases the model’s sensitivity to subtle differences. \\ K-Nearest Neighbors (KNN) & A model with a single neighbor (K = 1, i.e., NN) and Euclidean distance metric. Effective \\ Naive Bayes & A probabilistic model based on kernel density estimation, assuming feature independence. \\ Neural Network (MLP) & A single-layer neural network with 50 neurons in the hidden layer, trained without regularization. It uses labels transformed into one-hot encoded vectors. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview of the applied classifiers. assessing the informativeness of the parameters yielded varying results. In the authors' opinion, both the Random Forest test and the Kruskal-Wallis test proved to be relatively practical tools in the feature identification process in this study, as they enabled automatic ranking that was relatively consistent with the empirical structure of the data. Figure 4 presents feature rankings obtained using the Kruskal-Wallis (KW) test and the Random Forest (RF) method. It is worth noting that in both the Kruskal-Wallis test and the Random Forest method, the features with high statistical significance aligned with the intuition of geological experts, highlighting differences in grain size and texture correlation. It is essential to note that, when analyzing object-based features, the uniform segmentation algorithm frequently returned single-pixel objects or failed to accurately separate structures. This is particularly relevant, as geological images represent a challenging case for segmentation, usually requiring dedicated, specialized segmentation methods tailored to different rock types. Introducing a universal and robust segmentation algorithm into the pipeline would likely improve the accuracy of the discriminative models. In the present study, this step was omitted in favor of a simplified approach: the aim was to analyze the size of coherent color clusters as a potentially distinguishing indicator between synthetic and real data. Features based on the statistics of these clusters often achieved high informational value; however, it should be emphasized that they were also sensitive to missing data, particularly in cases where segmentation failed to identify any coherent objects. This necessitated decisions regarding the elimination of certain features or samples. It was also observed that  
Figure 4: Feature importance ranking scatter for the Kruskal-Wallis test and Random Forest classifier. some features varied in their ability to distinguish between real and generated images. A consistent trend across all analyzed groups was the noticeable dispersion of feature values in real images. In contrast, generated images exhibited greater uniformity, with their values tightly clustered around the mean. Interesting results were observed during the empirical analysis of the parameters. A particularly noteworthy finding was the relatively high separability of the DFT Center To Edge Ratio, \(C/E\) (Equations (1) and (2)) parameter, calculated in the frequency domain. This feature was computed as the central to peripheral energy ratio, expressed as a function of radius \(r\) (Equations (1) and (2)).