| Project Detail |
Although scientific methods to analyze cultural heritage are being developed rapidly, research in the field of forgery detection (art created with the intent to deceive) is too fragmentary and discipline-bound to face the current scholarly confusion and information overload. This project lays the intellectual foundation for a new type of connoisseurship, building on the latest developments in (technical) art history and data science. Art historians are used to focus on the past and the present. This project will allow art experts –for the first time– to face forward and predict high risks. The goal is to first generate fundamental new insights into significant anomalies that betray forged paintings (as well as into key characteristics of comparable originals); secondly, to develop ‘thin-slicing’ tools, i.e. computer programs and checklists that help to quickly and effectively select potential forgeries for in-depth analysis; and ultimately, to give connoisseurship a new reach and relevance through the novel, integrated approach to assess, categorize and interpret paintings.
Despite the great importance of connoisseurship for art history (providing the basic classification of who created what and when, information essential for virtually all interpretative studies in this field), its methodology has not kept abreast with innovative multi-disciplinary approaches to collaboration among experts in different fields of inquiry. The 21st century has witnessed major breakthroughs in thinking about authenticity within art history; the scientific methods and technical means to examine artworks have improved tremendously; and data science and data visualization techniques allow us to think on an unprecedented scale. Building on classified research for the French Ministry of Justice, two recent Dutch Research Council (NWO) projects and extensive experience, I am in a unique position to forge effective multi-disciplinary collaborations and spearhead this project. |
