Research
Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks
arXiv:2608.28671v1 Announce Type: cross Abstract: AI-generated art has sparked debates around potential plagiarism, as these images may closely resemble existing artworks. This research quantifies the
arXiv:2608.28671v1 Announce Type: cross 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 (elta mu = 0.677), demonstrating the effectiveness of our semantic-visual embeddings.
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Source: arXiv cs.AI | 2026-09-01