Art Recognition is a technology company headquartered in Adliswil, within the Zurich metropolitan area, Switzerland. Specializing in the application of artificial intelligence (AI) for the purposes of art authentication and the detection of art forgeries, Art Recognition integrates advanced algorithms and computer vision technology. The company's operations extend globally, with a primary aim to increase transparency and security in the art market.

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Art Recognition was established in 2019 by Dr. Carina Popovici and Christiane Hoppe-Oehl. The foundation of the company was driven by the long-standing challenge in the art world of authenticating paintings, a process traditionally reliant on expert judgment, historical research, and scientific analysis. Recognizing the limitations of existing methods, the co-founders were motivated by technological advancements in digital imaging and pattern recognition algorithms in the field of art.

These technological advancements, particularly in the realm of high-resolution digital imagery, enable a more detailed examination of artworks.^[1] By analyzing brushstrokes, signature patterns, and other distinct characteristics, and comparing them with known works by the same artist, digital tools offer a new dimension in authentication. Popovici and Hoppe-Oehl aimed to develop an advanced algorithm that could further assist experts by identifying stylistic elements and patterns unique to individual artists, thus aiding in the art authentication process.

Art Recognition employs a combination of machine learning techniques, computer vision algorithms, and deep neural networks to assess the authenticity of artworks.^[2] The AI algorithm analyzes various visual characteristics, such as brushstrokes, color palette, texture, and composition, to identify patterns and similarities with known authentic artworks.

The company's technology undergoes a process of data collection, dataset preparation, and training. In the initial phase, datasets are compiled, and data selection is supervised by art historians to ensure the inclusion of genuine artworks by specific artists. This approach aims to avoid including artworks that may have been partially completed by apprentices or contain mixed authorship.

Upon the preparation of datasets, a segment of the image set is used for training the AI algorithm, while the remaining images are set aside for testing. This phase aims to ensure the algorithm's proficiency in distinguishing authentic artworks from forgeries. Post-training, the algorithm undergoes evaluation with the test data, assessing its accuracy and efficacy in authenticating artworks.

After the testing phase, the AI algorithm is applied to analyze new images, including submissions from clients. Additionally, the algorithm is designed to identify artworks generated by generative AI, mimicking the style of renowned artists. This capability equips the algorithm to withstand adversarial attacks, enhancing its reliability in differentiating between authentic and artificially generated fake art pieces.^[3]

Art Recognition's collaboration with Tilburg University in The Netherlands has resulted in the acquisition of a research grant from Eurostars,Eureka (organisation) the Eureka's flagship small and medium-sized enterprises (SME) funding program. In addition, the company has formed a partnership with the University of Liverpool in the United Kingdom, which has been supported by the Science and Technology Facilities Council (STFC) Impact Acceleration Award. Furthermore, Art Recognition has established a relationship with Innosuisse, a Swiss innovation agency,^[4] to expand its research and development initiatives.

Art Recognition's AI algorithm has received attention from various media outlets and industry events. The company was featured on the front page of The Wall Street Journal^[5] for its involvement in the authentication case of the Flaget Madonna, believed to have been partly painted by Raphael.

A broadcast by the Swiss public television SRF showcased how the algorithm can be used to detect art forgeries with high accuracy.^[6] Additionally, the company's work was featured in a TEDx talk discussing the use of AI in art authentication.

The technology developed by Art Recognition has been recognized for its role in providing a technology-based art authentication solution, compared to traditional methods. This advancement is seen as significant in the field of art verification, offering a modern approach to a historically complex process.^[7]

The use of AI in art authentication, as pioneered by Art Recognition, has become a topic of professional discourse. Notably, this subject was the focus of a debate on Radio Télévision Suisse, where experts deliberated over the capabilities and limitations of AI in identifying art forgeries. Such discussions highlight the evolving landscape of art authentication in the age of digital technology.^[8]

Despite the advancements in AI-driven art authentication, the field continues to face unique challenges, particularly regarding the acceptance of such technologies. Experts in the field stress the necessity of using AI as a complementary tool alongside traditional methods, rather than as a stand-alone or definitive solution for authenticating art.^[9]

Art Recognition's AI algorithm has been applied to several high-profile and controversial artworks, sparking significant interest and debate in the art world.

1. Samson and Delilah at the National Gallery in London: The National Gallery's "Samson and Delilah", traditionally attributed to the artist Rubens, has also been examined using Art Recognition's AI, which has assessed the painting as non-authentic. This analysis contributed to ongoing scholarly discussions regarding the work's authenticity.^[10]
2. De Brecy Tondo Madonna. A research team from Bradford University and Nottingham University initially attributed the painting to Raphael, employing an AI face recognition software,^[11] while the AI developed at Art Recognition returned a negative result.^[12] As the face recognition method proved inadequate for art authentication,^[13] the Bradford group developed a new technology more akin to the approach used by Art Recognition. Notably, a crucial difference emerged in the datasets used to train the respective AI systems. While the Bradford group's AI was trained using 49 images, Art Recognition utilized a substantially larger dataset of over 100 images. This difference in the size and composition of the training datasets underscored the significant impact that data selection has on the outcomes of AI-driven art analysis.^[14]
3. Lucian Freud Painting Controversy: Featured in The New Yorker, a painting attributed to Lucian Freud became a subject of dispute. Art Recognition's AI analysis played a pivotal role in examining the painting's authenticity, contributing to the broader discussion about the challenges in verifying modern artworks.^[15]
4. Titian at Kunsthaus Zürich: A painting attributed to Titian, housed at Kunsthaus Zürich, has been a topic of debate among art experts. The application of Art Recognition's technology offered a new perspective, utilizing AI to analyze the painting's stylistic elements in comparison with authenticated works of Titian. Following this debate, Kunsthaus Zürich has announced plans to initiate a comprehensive project aimed at resolving the authenticity questions surrounding the painting. This project is set to involve collaboration with scientists and technology companies, leveraging a multidisciplinary approach to authenticate the artwork.^[16]

In each of these instances, Art Recognition's involvement has provided additional perspectives through AI analysis while contributing to broader conversations about the role of technology in art authentication. These cases demonstrate the evolving nature of art verification, where traditional methods are being supplemented, and sometimes challenged, by new technological approaches. However, they also underline the ongoing debates about the acceptance of AI in the field of art history, especially in the authentication of works by renowned artists.