Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929560634785792 |
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| author | Guo, Zhongliang Dong, Junhao Qian, Yifei Wang, Kaixuan Li, Weiye Guo, Ziheng Wang, Yuheng Li, Yanli Arandjelović, Ognjen Fang, Lei |
| author_facet | Guo, Zhongliang Dong, Junhao Qian, Yifei Wang, Kaixuan Li, Weiye Guo, Ziheng Wang, Yuheng Li, Yanli Arandjelović, Ognjen Fang, Lei |
| contents | Neural style transfer (NST) generates new images by combining the style of one image with the content of another. However, unauthorized NST can exploit artwork, raising concerns about artists' rights and motivating the development of proactive protection methods. We propose Locally Adaptive Adversarial Color Attack (LAACA), empowering artists to protect their artwork from unauthorized style transfer by processing before public release. By delving into the intricacies of human visual perception and the role of different frequency components, our method strategically introduces frequency-adaptive perturbations in the image. These perturbations significantly degrade the generation quality of NST while maintaining an acceptable level of visual change in the original image, ensuring that potential infringers are discouraged from using the protected artworks, because of its bad NST generation quality. Additionally, existing metrics often overlook the importance of color fidelity in evaluating color-mattered tasks, such as the quality of NST-generated images, which is crucial in the context of artistic works. To comprehensively assess the color-mattered tasks, we propose the Adversarial Color Distance Metric (ACDM), designed to quantify the color difference of images pre- and post-manipulations. Experimental results confirm that attacking NST using LAACA results in visually inferior style transfer, and the ACDM can efficiently measure color-mattered tasks. By providing artists with a tool to safeguard their intellectual property, our work relieves the socio-technical challenges posed by the misuse of NST in the art community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09673 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack Guo, Zhongliang Dong, Junhao Qian, Yifei Wang, Kaixuan Li, Weiye Guo, Ziheng Wang, Yuheng Li, Yanli Arandjelović, Ognjen Fang, Lei Computer Vision and Pattern Recognition Cryptography and Security Machine Learning Image and Video Processing Neural style transfer (NST) generates new images by combining the style of one image with the content of another. However, unauthorized NST can exploit artwork, raising concerns about artists' rights and motivating the development of proactive protection methods. We propose Locally Adaptive Adversarial Color Attack (LAACA), empowering artists to protect their artwork from unauthorized style transfer by processing before public release. By delving into the intricacies of human visual perception and the role of different frequency components, our method strategically introduces frequency-adaptive perturbations in the image. These perturbations significantly degrade the generation quality of NST while maintaining an acceptable level of visual change in the original image, ensuring that potential infringers are discouraged from using the protected artworks, because of its bad NST generation quality. Additionally, existing metrics often overlook the importance of color fidelity in evaluating color-mattered tasks, such as the quality of NST-generated images, which is crucial in the context of artistic works. To comprehensively assess the color-mattered tasks, we propose the Adversarial Color Distance Metric (ACDM), designed to quantify the color difference of images pre- and post-manipulations. Experimental results confirm that attacking NST using LAACA results in visually inferior style transfer, and the ACDM can efficiently measure color-mattered tasks. By providing artists with a tool to safeguard their intellectual property, our work relieves the socio-technical challenges posed by the misuse of NST in the art community. |
| title | Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2401.09673 |