Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchain
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912497591648256 |
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| author | Wang, Yiquan |
| author_facet | Wang, Yiquan |
| contents | The digital art market faces unprecedented challenges in authenticity verification and copyright protection. This study introduces an integrated framework to address these issues by combining neural style transfer, fractal analysis, and blockchain technology. We generate abstract artworks inspired by Jackson Pollock, using their inherent mathematical complexity to create robust, imperceptible watermarks. Our method embeds these watermarks, derived from fractal and turbulence features, directly into the artwork's structure. This approach is then secured by linking the watermark to NFT metadata, ensuring immutable proof of ownership. Rigorous testing shows our feature-based watermarking achieves a 76.2% average detection rate against common attacks, significantly outperforming traditional methods (27.8-44.0%). This work offers a practical solution for digital artists and collectors, enhancing security and trust in the digital art ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchain Wang, Yiquan Computer Vision and Pattern Recognition The digital art market faces unprecedented challenges in authenticity verification and copyright protection. This study introduces an integrated framework to address these issues by combining neural style transfer, fractal analysis, and blockchain technology. We generate abstract artworks inspired by Jackson Pollock, using their inherent mathematical complexity to create robust, imperceptible watermarks. Our method embeds these watermarks, derived from fractal and turbulence features, directly into the artwork's structure. This approach is then secured by linking the watermark to NFT metadata, ensuring immutable proof of ownership. Rigorous testing shows our feature-based watermarking achieves a 76.2% average detection rate against common attacks, significantly outperforming traditional methods (27.8-44.0%). This work offers a practical solution for digital artists and collectors, enhancing security and trust in the digital art ecosystem. |
| title | Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchain |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.20519 |