@misc{10481/109652,
year = {2026},
month = {1},
url = {https://hdl.handle.net/10481/109652},
abstract = {Painting authentication is an inherently complex task, often relying on a combination
of connoisseurship and technical analysis. This study focuses on the authentication of
a single painting attributed to Paolo Veronese, using a convolutional neural network
approach tailored to severe data scarcity. To ensure that stylistic comparisons were
based on artistic execution rather than iconographic differences, the dataset was
restricted to paintings depicting the Holy Family, the same subject as the work
under authentication. A custom shallow convolutional network was developed
to process multichannel inputs (RGB, grayscale, and edge maps) extracted from
overlapping patches via a sliding-window strategy. This patch-based design expanded
the dataset from a small number of paintings to thousands of localized patches,
enabling the model to learn microtextural and brushstroke features. Regularization
techniques were employed to enhance generalization, while a painting-level
cross-validation strategy was used to prevent data leakage. The model achieved
high classification performance (accuracy of 94.51%, Area under the Curve 0.99)
and generated probability heatmaps that revealed stylistic coherence in authentic
Veronese works and fragmentation in non-Veronese paintings. The work under
examination yielded an intermediate global mean Veronese probability (61%) with
extensive high-probability regions over stylistically salient passages, suggesting
partial stylistic affinity. The results support the use of patch-based models for
stylistic analysis in art authentication, especially under domain-specific data
constraints. While the network provides strong probabilistic evidence of stylistic
affinity, definitive attribution requires further integration with historical, technical,
and provenance-based analyses.},
publisher = {Frontiers},
keywords = {convolutional neural networks},
keywords = {painting classification},
keywords = {Paolo Veronese},
keywords = {sliding-window patch extraction},
keywords = {Authentication},
title = {Painting Authentication Using CNNs and Sliding Window Feature Extraction},
doi = {10.3389/frai.2025.1738444},
author = {Ruiz de Miras, Juan and Vílchez Quero, José Luis and Gacto Colorado, María José and Martín Perandrés, Domingo},
}