A kind of “arms race” has emerged, pitting art emulation against detection. Authenticators are falling far behind forgers. The number of connoisseurs is diminishing, and detection technologies are largely unchanged from where they were two decades ago.
In its determination to win the technological “arms race”, we are helping Hephaestus develop the next generation of tools to detect misattributions and eradicate forgery. Machine learning has the potential to become an important tool given its objectivity, unbiased approach and ability to 'see' things that the eye cannot.
In order to make these inferences, the machine needs high quality data to be trained on. We are developing the techniques and tools required to generate this data. For example, we are building a 3D painting scanner that is able to obtain depth data from artworks, helping the machine determine the way that paint was applied to the canvas.
We have also designed their website and preliminary materials and content.