Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks

Diego Castro Elvira; Navil Pineda Rugerio; Jesús García-Ramírez; Cecilia Reyes-Peña; Ricardo Ramos-Aguilar

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arXiv · 2608.28671

Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks

Diego Castro Elvira·Navil Pineda Rugerio·Jesús García-Ramírez·Cecilia Reyes-Peña·Ricardo Ramos-Aguilar

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Abstract

AI-generated art has sparked debates around potential plagiarism, as these images may closely resemble existing artworks. This research quantifies the similarity between original pieces and AI-generated counterparts, particularly those produced by the Stable Diffusion XL Refiner 1.0. We use Siamese Networks with frozen CLIP encoders and cosine similarity optimized through triplet loss. A dataset of paired original and generated images was built using image-to-image generation and custom prompts, enriched with semantic descriptors and BLIP-2 captions. Prior studies report up to 81\% style replication and 90\% visual similarity. Our results show high discriminative performance: training accuracy reached 99.9\%, and the best model configuration achieved 99.4\% test accuracy with strong inter-class separation ($δμ$ = 0.677), demonstrating the effectiveness of our semantic-visual embeddings.

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Diego Castro Elvira, Navil Pineda Rugerio, Jesús García-Ramírez, Cecilia Reyes-Peña, Ricardo Ramos-Aguilar. 2026-08-24. Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks. https://arxiv.org/abs/2608.28671

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