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

• URL: http://arxiv.org/abs/2608.28671v1
• Date: Mon, 24 Aug 2026 19:42:35 GMT
• Title: Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks
• Abstract summary: This research quantifies the similarity between original pieces and AI-generated counterparts.<n>We use Siamese Networks with frozen CLIP encoders and cosine similarity optimized through triplet loss.
• Score: 0.06524460254566902
• License: http://creativecommons.org/licenses/by/4.0/
• 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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