StyleSentinel: Reliable Artistic Copyright Verification via Stylistic Fingerprints

TL;DR

StyleSentinel introduces a novel method for verifying artistic copyright by extracting and modeling an artist's unique stylistic fingerprint, enabling robust protection even with minimal samples and online artwork.

Contribution

It proposes a new approach that uses stylistic fingerprints and one-class learning to improve copyright verification of artwork, overcoming limitations of previous watermark-based methods.

Findings

Outperforms state-of-the-art in one-sample verification tasks.

Effective in online platform scenarios.

Robust against common artistic variations.

Abstract

The versatility of diffusion models in generating customized images has led to unauthorized usage of personal artwork, which poses a significant threat to the intellectual property of artists. Existing approaches relying on embedding additional information, such as perturbations, watermarks, and backdoors, suffer from limited defensive capabilities and fail to protect artwork published online. In this paper, we propose StyleSentinel, an approach for copyright protection of artwork by verifying an inherent stylistic fingerprint in the artist's artwork. Specifically, we employ a semantic self-reconstruction process to enhance stylistic expressiveness within the artwork, which establishes a dense and style-consistent manifold foundation for feature learning. Subsequently, we adaptively fuse multi-layer image features to encode abstract artistic style into a compact stylistic fingerprint. Finally, we model the target artist's style as a minimal enclosing hypersphere boundary in the feature space, transforming complex copyright verification into a robust one-class learning task. Extensive experiments demonstrate that compared with the state-of-the-art, StyleSentinel achieves superior performance on the one-sample verification task. We also demonstrate the effectiveness through online platforms.