Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization
Art authentication has traditionally required deep expertise and knowledge of an artist’s work. Recently, computer vision algorithms have shown promise in image processing tasks; however, creating an automated model for painting authentication remains a challenge in art preservation and history. The...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/75 |
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| author | Elhamsadat Azimi Amirsaman Ashtari Jaehong Ahn |
| author_facet | Elhamsadat Azimi Amirsaman Ashtari Jaehong Ahn |
| author_sort | Elhamsadat Azimi |
| collection | DOAJ |
| description | Art authentication has traditionally required deep expertise and knowledge of an artist’s work. Recently, computer vision algorithms have shown promise in image processing tasks; however, creating an automated model for painting authentication remains a challenge in art preservation and history. The challenge is heightened as forgers cleverly create artworks that imitate the original artist’s unique brushstroke signature while introducing new content. To address this and to emphasize the importance of the artist’s unique brushstroke signature, we present a model leveraging conditional Generative Adversarial Networks, trained on in-depth visualization of an artist’s brushstroke style. Two methods including forgery scores computation using frequency analysis and trained discriminator models are proposed to identify counterfeiting. To visualize the depth of the brushstrokes datasets, we use the Reflectance Transformation Imaging technique. We evaluate the authentication of oil paintings by a contemporary Korean artist at the level of image patches in two scenarios. First, we distinguish an original painting from its corresponding counterfeit. Second, we detect a forged painting with creative content that mimics the original brushstroke signature. Results suggest that Generative Adversarial Networks trained on in-depth information have the potential to augment traditional methods in art authentication when utilized by connoisseurs. |
| format | Article |
| id | doaj-art-57b0ba8cb4ce40acacd7c1d56a6af5a3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-57b0ba8cb4ce40acacd7c1d56a6af5a32025-01-10T13:14:21ZengMDPI AGApplied Sciences2076-34172024-12-011517510.3390/app15010075Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth VisualizationElhamsadat Azimi0Amirsaman Ashtari1Jaehong Ahn2Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Republic of KoreaGraduate School of Culture Technology, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Republic of KoreaGraduate School of Culture Technology, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Republic of KoreaArt authentication has traditionally required deep expertise and knowledge of an artist’s work. Recently, computer vision algorithms have shown promise in image processing tasks; however, creating an automated model for painting authentication remains a challenge in art preservation and history. The challenge is heightened as forgers cleverly create artworks that imitate the original artist’s unique brushstroke signature while introducing new content. To address this and to emphasize the importance of the artist’s unique brushstroke signature, we present a model leveraging conditional Generative Adversarial Networks, trained on in-depth visualization of an artist’s brushstroke style. Two methods including forgery scores computation using frequency analysis and trained discriminator models are proposed to identify counterfeiting. To visualize the depth of the brushstrokes datasets, we use the Reflectance Transformation Imaging technique. We evaluate the authentication of oil paintings by a contemporary Korean artist at the level of image patches in two scenarios. First, we distinguish an original painting from its corresponding counterfeit. Second, we detect a forged painting with creative content that mimics the original brushstroke signature. Results suggest that Generative Adversarial Networks trained on in-depth information have the potential to augment traditional methods in art authentication when utilized by connoisseurs.https://www.mdpi.com/2076-3417/15/1/75patch-level oil painting’s forgery detectionconditional Generative Adversarial Networksfrequency analysisdepth information visualizationReflectance Transformation Imaging |
| spellingShingle | Elhamsadat Azimi Amirsaman Ashtari Jaehong Ahn Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization Applied Sciences patch-level oil painting’s forgery detection conditional Generative Adversarial Networks frequency analysis depth information visualization Reflectance Transformation Imaging |
| title | Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization |
| title_full | Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization |
| title_fullStr | Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization |
| title_full_unstemmed | Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization |
| title_short | Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization |
| title_sort | patch based oil painting forgery detection based on brushstroke analysis using generative adversarial networks and depth visualization |
| topic | patch-level oil painting’s forgery detection conditional Generative Adversarial Networks frequency analysis depth information visualization Reflectance Transformation Imaging |
| url | https://www.mdpi.com/2076-3417/15/1/75 |
| work_keys_str_mv | AT elhamsadatazimi patchbasedoilpaintingforgerydetectionbasedonbrushstrokeanalysisusinggenerativeadversarialnetworksanddepthvisualization AT amirsamanashtari patchbasedoilpaintingforgerydetectionbasedonbrushstrokeanalysisusinggenerativeadversarialnetworksanddepthvisualization AT jaehongahn patchbasedoilpaintingforgerydetectionbasedonbrushstrokeanalysisusinggenerativeadversarialnetworksanddepthvisualization |