Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchain

TL;DR

This paper presents a novel framework combining neural style transfer, fractal analysis, and blockchain to embed and verify robust watermarks in AI-generated art, enhancing authenticity and copyright protection.

Contribution

It introduces a new method of embedding fractal-based watermarks into digital art and linking them to blockchain for secure ownership verification.

Findings

Watermark detection rate of 76.2% against attacks

Outperforms traditional watermarking methods (27.8-44.0%)

Provides a practical solution for digital art security

Abstract

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.