# Identification of AI-Generated Rock Thin-Section Images by Feature Analysis Under Data Scarcity
## 1 Introduction  
In recent years, there has been rapid advancement in generative artificial intelligence (GAI) technologies, which enable the creation of realistic images, texts, and other types of synthetic data. GAI models, such as Generative Adversarial Networks (GANs) and diffusion models, are increasingly being applied across various fields of science and research technology [1]. In the domain of geosciences, this development creates new opportunities for image-based analysis, particularly in cases where access to empirical data is limited or expensive. One of the key aspects where image data is utilized in geology is the microscopic analysis of rocks. Microscopic techniques enable the identification and characterization of mineral components and textural structures, which are fundamental in lithology, petrography, and geochemistry studies [2]. This analysis can be supported by computational methods [3], including the classification of microscopic rock components [4], coal macreal identification [5], or even unconventional image-based search approaches [6,7]. Computer image analysis of microscopic thin-section images enables the precise determination of the area content of individual mineral phases. Applying machine learning methods ingeological image analysis offers powerful tools to support scientific research. In particular, they facilitate advancements such as rock texture classification [8; 9] and lithological identification [10; 11]. However, in most cases, due to the high variability of rocks, manual adjustment of the code to the given rock and mineral type, or at least additional verification, is required. Also, effectively implementing these solutions requires large, diverse, and well-annotated datasets. Unfortunately, access to high-quality field data is often limited, particularly in remote or deeply situated locations, and rare geological structures appear infrequently. Moreover, obtaining empirical data requires a significant time investment and is not always possible (very rarely minerals or rocks). In response to these limitations, synthetic data is being increasingly used to train and test interpretive algorithms. Generative models enable the creation of images that replicate complex geological configurations, such as faults, uncommon stratigraphic sequences, or mineral deposits [12; 13]. This promotes greater diversity in datasets and improves the modeling of rare or hard-to-access scenarios [14; 15]. Synthetic data is also widely used in data augmentation as a method for increasing the representativeness of small datasets and enhancing the performance of training algorithms [16; 17; 18; 19; 20]. The development of generative artificial intelligence enables the transformation of one data modality into another, depending on the specific task. This includes models such as text-to-image (creating images based on textual descriptions), image-to-image (transforming images using patterns from other images), and text-to-code or text-to-text [21]. The growing accessibility of such tools, including pre-trained models available through open repositories like Hugging Face, means their use is no longer limited to specialists but is increasingly accessible to non-domain users. As the realism of synthetic images increases, a new risk emerges: the possibility of their unintentional use as real data. GAI models can generate images that replicate real-world patterns with such a high degree of realism that they may be mistaken for authenticity. The research into synthetic data detection is gaining increasing importance [22], particularly in fields such as medicine [23], fake information detection [23], and the identification of generated human faces [24; 25]. While initial efforts are also emerging in geoinformation data, such as satellite image analysis [26], little attention has been paid to microscopic images in environmental sciences. This can influence the reliability of scientific or engineering analyses.