Specialized Image Descriptors Adaptation for Generated Images Recognition

This study introduces an innovative method for recognizing automatically generated images by utilizing adapted descriptors specifically designed to analyze unique structural and morphological features characteristic of artificially created content. The methodology focuses on analyzing features inherent to image generation processes, ensuring the optimization of descriptors for identifying complex and subtle patterns associated with generative algorithms. The integration of these specialized descriptors not only enhances recognition accuracy but also enables the extraction of interpretable features that provide deeper insights into the key principles of artificial content creation. To validate the effectiveness of the proposed method, an annotated dataset was developed and used to test and compare performance against various classification algorithms, including deep learning-based neural networks. Experimental results demonstrated that the proposed approach achieves high efficiency. These findings underscore the significance of the proposed methodology in advancing automated systems for the analysis of generated content in areas such as authenticity verification, media security, marketing, and digital validation while maintaining high computational efficiency and interpretability of results.