Enhancing Authorship Attribution with Synthetic Paintings

• URL: http://arxiv.org/abs/2603.04343v1
• Date: Wed, 04 Mar 2026 18:00:42 GMT
• Title: Enhancing Authorship Attribution with Synthetic Paintings
• Abstract summary: We propose a hybrid approach that combines real and synthetic data to enhance model accuracy and generalization across artistic styles.<n> Experimental results show that adding synthetic images leads to higher ROC-AUC and accuracy compared to using only real paintings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attributing authorship to paintings is a historically complex task, and one of its main challenges is the limited availability of real artworks for training computational models. This study investigates whether synthetic images, generated through DreamBooth fine-tuning of Stable Diffusion, can improve the performance of classification models in this context. We propose a hybrid approach that combines real and synthetic data to enhance model accuracy and generalization across similar artistic styles. Experimental results show that adding synthetic images leads to higher ROC-AUC and accuracy compared to using only real paintings. By integrating generative and discriminative methods, this work contributes to the development of computer vision techniques for artwork authentication in data-scarce scenarios.

Related papers

• Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset [0.4784604186682396]
  This study systematically analyzes the differences between two types of synthetic images created by different generation methods and real images from three perspectives.<n>It experimentally verifies how synthetic data should be utilized by considering realistic data mixing scenarios.
  arXiv  Detail & Related papers  (2026-07-13T21:18:15Z)
• What Makes Synthetic Data Effective in Image Segmentation [35.90116236392466]
  Synthetic data has emerged as a promising solution for visual understanding.<n>In this work, we conduct a systematic analysis of synthetic images from state-of-the-art diffusion models to uncover the factors governing their utility.<n>We propose SENSE, a unified framework that leverages flexible and scalable synthetic data to substantially enhance segmentation performance.
  arXiv  Detail & Related papers  (2026-05-19T03:07:04Z)
• From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data [63.40634698361175]
  Makeup transfer aims to apply the makeup style of a reference portrait to a source portrait while preserving identity and background.<n>Recent diffusion- and flow-based approaches instead exploit synthetic data for supervised training, leading to significant improvements.<n>This paper proposes ConsistentBeauty and RealBeauty, a synthetic-to-real post-training framework.
  arXiv  Detail & Related papers  (2026-05-08T15:21:43Z)
• Provably Improving Generalization of Few-Shot Models with Synthetic Data [15.33628135372502]
  We develop a theoretical framework that quantifies the impact of distribution discrepancies on supervised learning.<n>We propose a novel theoretical-based algorithm that integrates prototype learning to optimize both data partitioning and model training.
  arXiv  Detail & Related papers  (2025-05-30T03:59:45Z)
• A Tiered GAN Approach for Monet-Style Image Generation [0.562479170374811]
  This paper introduces a tiered GAN model to progressively refine image quality through a multi-stage process.<n>The model transforms random noise into detailed artistic representations, addressing common challenges such as instability in training, mode collapse, and output quality.
  arXiv  Detail & Related papers  (2024-12-07T19:10:29Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Scaling Laws of Synthetic Images for Model Training ... for Now [54.43596959598466]
  We study the scaling laws of synthetic images generated by state of the art text-to-image models. We observe that synthetic images demonstrate a scaling trend similar to, but slightly less effective than, real images in CLIP training.
  arXiv  Detail & Related papers  (2023-12-07T18:59:59Z)
• Improving the Effectiveness of Deep Generative Data [5.856292656853396]
  Training a model on purely synthetic images for downstream image processing tasks results in an undesired performance drop compared to training on real data. We propose a new taxonomy to describe factors contributing to this commonly observed phenomenon and investigate it on the popular CIFAR-10 dataset. Our method outperforms baselines on downstream classification tasks both in case of training on synthetic only (Synthetic-to-Real) and training on a mix of real and synthetic data.
  arXiv  Detail & Related papers  (2023-11-07T12:57:58Z)
• Person Image Synthesis via Denoising Diffusion Model [116.34633988927429]
  We show how denoising diffusion models can be applied for high-fidelity person image synthesis. Our results on two large-scale benchmarks and a user study demonstrate the photorealism of our proposed approach under challenging scenarios.
  arXiv  Detail & Related papers  (2022-11-22T18:59:50Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder [73.1010640692609]
  We propose a VQ-VAE architecture model with a diffusion decoder (DiVAE) to work as the reconstructing component in image synthesis. Our model achieves state-of-the-art results and generates more photorealistic images specifically.
  arXiv  Detail & Related papers  (2022-06-01T10:39:12Z)
• Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition [67.9464567157846]
  We propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-06-29T12:53:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.