用分形特征和区块链给生成的波洛克风格画打隐形水印，防伪造。
Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchain
- 基于分形与湍流特征生成不可见水印，嵌入画作结构中。
- 对抗常见攻击时检测率达76.2%，远超传统方法（27.8%-44.0%）。
- 适合数字艺术家和收藏家，提升作品确权与信任度。
数字艺术市场面临真实性验证与版权保护的严峻挑战。本文提出一种集成框架，结合神经风格迁移、分形分析与区块链技术，生成受杰克逊·波洛克启发的抽象艺术作品。利用其内在数学复杂性，构建鲁棒且不可感知的水印，水印特征源自分形与湍流特性，并直接嵌入画作结构。该水印通过关联NFT元数据实现永久存证，确保所有权不可篡改。严格测试表明，该基于特征的水印在对抗常见攻击时平均检测率达76.2%，显著优于传统方法（27.8%-44.0%）。本工作为数字艺术家与收藏家提供实用解决方案，增强数字艺术生态的安全性与可信度。
原文摘要 · Abstract (English)
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.
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