# Synthetic images aid the recognition of human-made art forgeries

arXiv: https://arxiv.org/abs/2312.14998 · HTML: https://mlsanity.com/paper/2312.14998/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models](https://mlsanity.com/paper/2210.06998/) (2022)
- [Are GAN generated images easy to detect? A critical analysis of the state-of-the-art](https://mlsanity.com/paper/2104.02617/) (2021)
- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [Lighting (In)consistency of Paint by Text](https://mlsanity.com/paper/2207.13744/) (2022)
- [Perspective (In)consistency of Paint by Text](https://mlsanity.com/paper/2206.14617/) (2022)
- [Art Authentication with Vision Transformers](https://mlsanity.com/paper/2307.03039/) (2023)
- [Geometric Tight Frame based Stylometry for Art Authentication of van Gogh Paintings](https://mlsanity.com/paper/1407.0439/) (2014)
- [Paint it Black: Generating paintings from text descriptions](https://mlsanity.com/paper/2302.08808/) (2023)
- [Machine: The New Art Connoisseur](https://mlsanity.com/paper/1911.10091/) (2019)
- [Artist, Style And Year Classification Using Face Recognition And Clustering With Convolutional Neural Networks](https://mlsanity.com/paper/2012.01009/) (2020)

## Built on by (most cited first, 4 of 4)

- [fruit-SALAD: A Style Aligned Artwork Dataset to reveal similarity perception in image embeddings](https://mlsanity.com/paper/2406.01278/) (2024)
- [Art Forgery Detection using Kolmogorov Arnold and Convolutional Neural Networks](https://mlsanity.com/paper/2410.04866/) (2024)
- [SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition](https://mlsanity.com/paper/2607.18907/) (2026)
- [Conducting Stylistic Analysis of Paintings through an Art-History Agent](https://mlsanity.com/paper/2608.29644/) (2026)

## Abstract (arXiv)

Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator.