This paper presents a method for document authenticity verification based on the analysis of scanned images and embedding representations generated by a deep learning model. The proposed approach leverages features of the paper microstructure and characteristics of the scanned document to classify its originality. The study employed an embedding model provided by the Cohere platform, while the classifier was built using a lightweight fully connected neural network. Experiments showed that even with a minimal number of training samples (one per class), the model could effectively distinguish original documents from their copies. The training process was fast and stable, completing in less than two seconds, which highlights the potential for integration into real time systems. Moreover, it was demonstrated that the document profile can be permanently stored as an embedding vector, enabling later verification without the need to retain the original image. The proposed solution offers high accuracy, low computational demands, and strong resistance to forgery even when the original document is unavailable.