Artificial intelligence has to date been enlisted as a bogeyman in cultural circles: Software will take the jobs of writers and translators, and AI-generated images ring the death toll for illustrators and graphic designers. Yet there's a corner of high culture where AI is taking on a starring role as hero, not displacing the traditional protagonists--art experts and conservators--but adding a powerful, compelling weapon to their arsenal when it comes to fighting forgeries and misattributions. AI is already exceptionally good at recognizing and authenticating an artist's work, based on the analysis of a digital image of a painting alone. AI's objective analysis has thrown a wrench into this traditional hierarchy. If an algorithm can determine the authorship of an artwork with statistical probability, where does that leave the old-guard art historians whose reputations have been built on their subjective expertise?

The attribution of the author of an art piece is typically a laborious manual process, usually relying on subjective evaluations of expert figures. However, there are some situations in which quantitative features of the artwork can support these evaluations. The extraction of these features can sometimes be automated, for instance, with the use of Machine Learning (ML) techniques. An example of these features is represented by repeated, mechanically impressed patterns, called punches, present chiefly in 13th and 14th-century panel paintings from Tuscany. Previous research in art history showcased a strong connection between the shapes of punches and specific artists or workshops, suggesting the possibility of using these quantitative cues to support the attribution. In the present work, we first collect a dataset of large-scale images of these panel paintings. Then, using YOLOv10, a recent and popular object detection model, we train a ML pipeline to perform object detection on the punches contained in the images. Due to the large size of the images, the detection procedure is split across multiple frames by adopting a sliding-window approach with overlaps, after which the predictions are combined for the whole image using a custom non-maximal suppression routine. Our results indicate how art historians working in the field can reliably use our method for the identification and extraction of punches.

This paper proposes dialogue as a method for evaluating generative AI tools for culturally-situated creative practice, that recognizes the socially situated nature of art. Drawing on sociologist Howard Becker's concept of Art Worlds, this method expands the scope of traditional AI and creativity evaluations beyond benchmarks, user studies with crowd-workers, or focus groups conducted with artists. Our method involves two mutually informed dialogues: 1) 'dialogues with art worlds' placing artists in conversation with experts such as art historians, curators, and archivists, and 2)'dialogues with the machine,' facilitated through structured artist- and critic-led experimentation with state-of-the-art generative AI tools. We demonstrate the value of this method through a case study with artists and experts steeped in non-western art worlds, specifically the Persian Gulf. We trace how these dialogues help create culturally rich and situated forms of evaluation for representational possibilities of generative AI that mimic the reception of generative artwork in the broader art ecosystem. Putting artists in conversation with commentators also allow artists to shift their use of the tools to respond to their cultural and creative context. Our study can provide generative AI researchers an understanding of the complex dynamics of technology, human creativity and the socio-politics of art worlds, to build more inclusive machines for diverse art worlds.

Can you get excited about artificial intelligence (AI)? When I asked ChatGPT who my favourite artist was, it said I'd never publicly expressed a preference, because as an art historian I don't do "subjective opinions". Evidently, ChatGPT doesn't subscribe to The Art Newspaper. Even if it gave the correct answer--Van Dyck--I'd still not be excited. In a famous episode of the 1960s TV series The Prisoner, Patrick McGoohan's character is presented with an all-knowing computer which, he is told, will make man redundant.

People tend to rejoice in the disclosure of a secret. Or, at the very least, media outlets have come to realize that news of "mysteries solved" and "hidden treasures revealed" generate traffic and clicks. So I'm never surprised when I see AI-assisted revelations about famous masters' works of art go viral. Over the past year alone, I've come across articles highlighting how artificial intelligence recovered a "secret" painting of a "lost lover" of Italian painter Modigliani, "brought to life" a "hidden Picasso nude", "resurrected" Austrian painter Gustav Klimt's destroyed works and "restored" portions of Rembrandt's 1642 painting "The Night Watch."The As an art historian, I've become increasingly concerned about the coverage and circulation of these projects.

People tend to rejoice in the disclosure of a secret. Or, at the very least, media outlets have come to realize that news of "mysteries solved" and "hidden treasures revealed" generate traffic and clicks. So I'm never surprised when I see AI-assisted revelations about famous masters' works of art go viral. Over the past year alone, I've come across articles highlighting how artificial intelligence recovered a "secret" painting of a "lost lover" of Italian painter Modigliani, "brought to life" a "hidden Picasso nude", "resurrected" Austrian painter Gustav Klimt's destroyed works and "restored" portions of Rembrandt's 1642 painting "The Night Watch." As an art historian, I've become increasingly concerned about the coverage and circulation of these projects.

Researchers at TU Delft in the Netherlands have recently developed a convolutional neural network (CNN)-based model to reconstruct drawings that have deteriorated over time. In their study, published in Springer's Machine Vision and Applications, they specifically used the model to reconstruct some of Vincent Van Gogh's drawings that were ruined over the years due to ink fading and discoloration. "The Netherlands has an international reputation with respect to arts, with famous artists like Rembrandt, Mondrian and Van Gogh," Jan van der Lubbe, one of the researchers who carried out the study, told TechXplore. "Therefore, art historical research and research into how to preserve cultural heritage play an important role in the Netherlands." In recent years, a growing number of researchers have tried to develop machine learning techniques, such as CNNs, for the analysis of artworks. So far, these tools have primarily been used to identify the artist who created specific artworks or to determine whether paintings are real or fake.

Connect, download a free E-Book, watch a keynote, or browse my blog. All around the world the trade in art, both man made and machine made art, is booming, with even art made by new Artificial Intelligence (AI) programs getting in on the act after a painting by one sold for over $430,000 recently at Christies, but detecting art forgeries is still as hard and expensive as ever. At the moment, for example, art historians might bring suspect work into a lab for infrared spectroscopy, radiometric dating, gas chromatography, or a combination of tests. But now a new AI does away with all that and it can spot fakes just by looking at the strokes used to compose a piece. In a new paper, researchers from Rutgers University and the Atelier for Restoration & Research of Paintings in the Netherlands document how their AI system broke down almost 300 line drawings by Picasso, Matisse, Modigliani, and other famous artists into 80,000 individual strokes.

The public queues for hours at the Musée du Louvre in Paris to catch a glimpse of this much studied portrait. But, according to AI, it's not much to look at really. At least that is the outcome from a project being undertaken at the Art and Artificial Intelligence Lab at Rutgers University. The team at Rutgers has been using AI to analyze and create art for the past five years, studying around 80,000 different paintings by over 1,100 artists. One of the first outcomes from the research was the replication of known painting styles.

In late March, a judge in Wiesbaden, Germany, found herself playing the uncomfortable role of art critic. On trial before her were two men accused of forging paintings by artists including Kazimir Malevich and Wassily Kandinsky, whose angular, abstract compositions can now go for eight-figure prices. The case had been in progress for three and a half years and was seen by many as a test. A successful prosecution could help end an epidemic of forgeries – so-called miracle pictures that appear from nowhere – that have been plaguing the market in avant-garde Russian art. But as the trial reached its climax, it disintegrated into farce.