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

arXiv: https://arxiv.org/abs/2608.28671 · HTML: https://mlsanity.com/paper/2608.28671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [InfoNCE: Identifying the Gap Between Theory and Practice](https://mlsanity.com/paper/2407.00143/) (2024)
- [On Copyright Risks of Text-to-Image Diffusion Models](https://mlsanity.com/paper/2311.12803/) (2023)
- [Measuring the Success of Diffusion Models at Imitating Human Artists](https://mlsanity.com/paper/2307.04028/) (2023)
- [Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models](https://mlsanity.com/paper/2404.08030/) (2024)
- [ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13061/) (2025)
- [Image Retrieval and Pattern Spotting using Siamese Neural Network](https://mlsanity.com/paper/1906.09513/) (2019)

## Abstract (arXiv)

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 (δ μ = 0.677), demonstrating the effectiveness of our semantic-visual embeddings.