Painting Authentication Using CNNs and Sliding Window Feature Extraction Ruiz de Miras, Juan Vílchez Quero, José Luis Gacto, María José Martín Perandrés, Domingo convolutional neural networks painting classification Paolo Veronese sliding-window patch extraction Authentication 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. 2026-01-14T08:44:37Z 2026-01-14T08:44:37Z 2026-01-13 journal article Ruiz de Miras J, Vílchez JL, Gacto MJ and Martín D (2026) Painting authentication using CNNs and sliding window feature extraction. Front. Artif. Intell. 8:1738444. doi: 10.3389/frai.2025.1738444 https://hdl.handle.net/10481/109652 10.3389/frai.2025.1738444 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Frontiers