Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks
arXiv:2608.28671v1 Announce Type: new 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 ($\delta \mu$ = 0.677), demonstrating the effectiveness of our semantic-visual embeddings.
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[Submitted on 24 Aug 2026]
Title:Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks
View a PDF of the paper titled Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks, by Diego Castro Elvira and 4 other authors
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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 ($\delta \mu$ = 0.677), demonstrating the effectiveness of our semantic-visual embeddings.
Comments: Accepted to LatinX in AI Research Workshop at Neurips 2025
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.28671 [cs.CV]
(or arXiv:2608.28671v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.28671
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jesús García-Ramírez [view email] [v1] Mon, 24 Aug 2026 19:42:35 UTC (13,706 KB)
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