A deep learning approach for anomaly detection in X-ray images of paintings

Abstract

The intersection of technological advancements and cultural heritage studies has intensified the exploration of historical treasures, captivating historians and enthusiasts alike. Artificial intelligence now plays a key role in forensic art investigations by uncovering hidden patterns to detect forgeries. This study focuses on anomaly detection in X-ray images of paintings using the Ghent Altarpiece for training and testing purposes. We propose a novel model combining a Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM), a Nested U-Net, and a new dataset derived from the Altarpiece. The proposed architecture was benchmarked against several state-of-the-art deep learning techniques in anomaly detection. Our model achieved an accuracy of 0.8399 and an F1 score of 0.7869, outperforming other methods in both accuracy and computational efficiency. Results, validated by a domain expert, show strong precision and computational efficiency through semi-supervised learning.

The intersection of technological advancements and cultural heritage studies has intensified the exploration of historical treasures, captivating historians and enthusiasts alike. Artificial intelligence now plays a key role in forensic art investigations by uncovering hidden patterns to detect forgeries. This study focuses on anomaly detection in X-ray images of paintings using the Ghent Altarpiece for training and testing purposes. We propose a novel model combining a Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM), a Nested U-Net, and a new dataset derived from the Altarpiece. The proposed architecture was benchmarked against several state-of-the-art deep learning techniques in anomaly detection. Our model achieved an accuracy of 0.8399 and an F1 score of 0.7869, outperforming other methods in both accuracy and computational efficiency. Results, validated by a domain expert, show strong precision and computational efficiency through semi-supervised learning.