On art authentication and the Rijksmuseum challenge: A residual neural network approach

Dobbs, Todd; Raś, Zbigniew W.

doi:10.1016/j.eswa.2022.116933

Cited by 10 publications

(11 citation statements)

References 6 publications

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“…The performances fall within a limited range, 78 − 91%. The best-performing study of Table 1 [11] made use of the ResNet101 architecture [23]. Hence, we select ResNet101 as one of the CNN architectures for our experiments.…”

Section: Selection Of Architecturesmentioning

confidence: 99%

“…The recent outstanding art-classification results reported by Dobbs and Ras [11], as discussed in Section 1.2, has led us to choose ResNet101, the 101-layer version of ResNet [23], as a representative CNN for our van Gogh authentication task. Although ResNet101 represents the state-of-the-art in art classification, it may not be the most robust CNN available.…”

Section: Architectures and Training Proceduresmentioning

confidence: 99%

“…hoc feature extraction methods (including fractal analysis, wavelet coefficients, and edge detection) to represent visual artistic features such as brushstrokes, followed by a machine learning model trained on such features to distinguish the works of the artist from possibly similar works by other artists [4,5,6,7,8]. The excellent pattern-recognition abilities of Convolutional Neural Networks (CNNs) have led to a new wave of studies showing impressive performances on art-classification tasks [9,10,11] and many other visual tasks [12,13,14]. These studies involve complex CNN architectures that are trained on large digitized art collections, generally adding to the CNN a last dense (fully-connected) layer.…”

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confidence: 99%

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Art authentication with vision transformers

Schaerf,

Postma,

Popovici

2023

Neural Comput & Applic

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In recent years, transformers, initially developed for language, have been successfully applied to visual tasks. Vision transformers have been shown to push the state of the art in a wide range of tasks, including image classification, object detection, and semantic segmentation. While ample research has shown promising results in art attribution and art authentication tasks using convolutional neural networks, this paper examines whether the superiority of vision transformers extends to art authentication, improving, thus, the reliability of computer-based authentication of artworks. Using a carefully compiled dataset of authentic paintings by Vincent van Gogh and two contrast datasets, we compare the art authentication performances of Swin transformers with those of EfficientNet. Using a standard contrast set containing imitations and proxies (works by painters with styles closely related to van Gogh), we find that EfficientNet achieves the best performance overall. With a contrast set that only consists of imitations, we find the Swin transformer to be superior to EfficientNet by achieving an authentication accuracy of over 85%. These results lead us to conclude that vision transformers represent a strong and promising contender in art authentication, particularly in enhancing the computer-based ability to detect artistic imitations.

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Section: Selection Of Architecturesmentioning

confidence: 99%

Section: Architectures and Training Proceduresmentioning

confidence: 99%

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confidence: 99%

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Art authentication with vision transformers

Schaerf,

Postma,

Popovici

2023

Neural Comput & Applic

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“…Starting from late 1990s, various computer vision and image analysis techniques such as fractal analysis [4], wavelets [5][6][7], sparse coding [8], clustering-based segmentation [9] and tight frame method [10] were applied to extract characteristic features of individual artist's style automatically. More recently, the development of efficient classifier neural networks such as Convolutional Neural Networks (CNNs) allowed reaching very high accuracies in artwork attribution [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Synthetic images aid the recognition of human-made art forgeries

Ostmeyer,

Schaerf,

Buividovich

et al. 2024

PLoS ONE

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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.

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“…In the past five years, art authentication received increased attention due to artificial intelligence, digital image processing, forensic techniques, and legal cases. From an artificial intelligence perspective, supervised deep learning algorithms, when applied to images of paintings, have attained an accuracy of 67.78% in authenticating art for 90 artists using the WikiArt dataset [3], and an accuracy of 32.40% in authenticating art for 1199 artists using the Rijksmuseum dataset [4]. On the digital image processing front, an accuracy of 91.7% was achieved in authenticating art for two artists using the principal component analysis (PCA) and a custom van Gogh and Raphael dataset.…”

Section: Introductionmentioning

confidence: 99%

Contemporary Art Authentication with Large-Scale Classification

Dobbs,

Nayeem,

Cho

et al. 2023

BDCC

Self Cite

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Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible as provenance can be obscured or lost through anonymity, forgery, gifting, or theft of artwork. This paper presents an image-only art authentication attribute marker of contemporary art paintings for a very large number of artists. The experiments in this paper demonstrate that it is possible to use ML-generated models to authenticate contemporary art from 2368 to 100 artists with an accuracy of 48.97% to 91.23%, respectively. This is the largest effort for image-only art authentication to date, with respect to the number of artists involved and the accuracy of authentication.

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On art authentication and the Rijksmuseum challenge: A residual neural network approach

Cited by 10 publications

References 6 publications

Art authentication with vision transformers

Art authentication with vision transformers

Synthetic images aid the recognition of human-made art forgeries

Contemporary Art Authentication with Large-Scale Classification

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