## 2 Related Works  
Many studies have investigated physical approaches to identify the calibration data period [16] using carbon dating. Carbon dating was also applied for detecting anachronistic components in forgery samples and pigment chemical X-ray diffraction analysis [17]. Revealing hidden information from the origins of the canvas based on scanning Macro X-Ray Fluorescence (MA-XRF) was studied by [18]. A significant amount of time overhead and difficulty to scale make the physical analyses expensive methods for solving this issue. Some studies explored traditional approaches combined with other methods. The authors of [19] proposed a transparent, probabilistic framework that combines historical, artistic, and forensic expertise to enhance the reliability and acceptance of signature authentication in art and judicial contexts. The research in [20] explored global collaboration and alternative dispute resolution to settle authenticity disputes in art, reducing reliance on costly litigation. The study in [21] discussed the challenges of applying traditional connoisseurship in the context of a diverse global art market, emphasizing the role of cultural identity. The longstanding tension between traditional connoisseurship and scientific methodologies in art authentication was highlighted in [22]. The study [23] underscored the pivotal role of art experts in the authentication process despite challenges posed by technological advancements and market pressures. It emphasized the enduring importance of connoisseurship, experience, and professional judgment in distinguishing genuine works from forgeries.  
Various research has been previously conducted to address the problem of forgery detection with different motivations. Artist recognition based on their artworks was examined by [24; 25], aimed at various reasons such as automatically cataloging the museum's objects and forgery detection. The painting classification method based on segmented brushstroke features was used by [26] to detect both forgery and author. An automated pattern recognition model using CNN supported by OpenGAN and II-loss was designed to help experts classify decorative patterns, called punches, found on gold foils in Late-Medieval Tuscan panel paintings [27].  
To classify and identify an artist based on the artistic brushstroke style, CNN showed decent results by extracting high-level features of images so that each layer further down learns more abstractly. Painting classification based on the artist's brushstroke features using K-mean clustering and extracted edge features was implemented in [28]. Studies used both Deep CNN (DCNN) [1; 29] and Machine Learning (ML) [30] for artist identification based on the painter's artistic style using annotated dataset. Deep Neural Networks (DNNs) [31] showed a promising result for classifying and detecting counterfeit images in illegal target content. Many studies focused on distinguishing a specific artist from other painters; for instance, [32] differentiated [PERSON]'s style from other painters' artworks. Similarly, Ref. [33] detected [PERSON]'s artworks using CNN which automatically extracts discriminative visual patterns of his style. The research in [34] used the technique of color space and grayscale to classify the paintings based on artistic style and genre to identify forgeries. The authors of [35] extracted the information including spatial, color, local information, as well as the orientation of gradient and intensity of the image for classifying the artistic style of the artist. The research in [36] detected counterfeit paintings using supervised learning based on extracted features from Hidden-Markov-Tree while the image clarity variation was correlated with their authentication. A multitask classification approach was proposed with the purpose of identifying different attributes of the artworks such as artist, date, and genre automatically [37].