A Neural Network Looks at Leonardo's Salvator Mundi

We use convolutional neural networks (CNNs) to analyze authorship questions surrounding the works of Leonardo da Vinci -- in particular, Salvator Mundi, the world's most expensive painting and among the most controversial. Trained on the works of an artist under study and visually comparable works of other artists, our system can identify likely forgeries and shed light on attribution controversies. Leonardo's few extant paintings test the limits of our system and require corroborative techniques of testing and analysis.

Rembrandts and Robots: Using Neural Networks to Explore Authorship in Painting

We use convolutional neural networks to analyze authorship questions surrounding works of representational art. Trained on the works of an artist under study and visually comparable works of other artists, our system can identify forgeries and provide attributions. Our system can also assign classification probabilities within a painting, revealing mixed authorship and identifying regions painted by different hands.

Salient Slices: Improved Neural Network Training and Performance with Image Entropy

As a training and analysis strategy for convolutional neural networks (CNNs), we slice images into tiled segments and use, for training and prediction, segments that both satisfy a criterion of information diversity and contain sufficient content to support classification. In particular, we utilize image entropy as the diversity criterion. This ensures that each tile carries as much information diversity as the original image, and for many applications serves as an indicator of usefulness in classification. To make predictions, a probability aggregation framework is applied to probabilities assigned by the CNN to the input image tiles. This technique facilitates the use of large, high-resolution images that would be impractical to analyze unmodified; provides data augmentation for training, which is particularly valuable when image availability is limited; and the ensemble nature of the input for prediction enhances its accuracy.