StyleProtect: Safeguarding Artistic Identity in Fine-tuned Diffusion Models

2025-09-17

AI Summary

Fine-tuning diffusion models is prone to misuse for unauthorized replication of artistic styles, threatening artists’ creative identity and intellectual labor. Method: We propose a lightweight style protection framework that identifies and localizes cross-attention layers highly sensitive to artistic style—via attention activation magnitude analysis and external feature correlation assessment—to jointly model style and content representations. During fine-tuning, only parameters in these critical layers are selectively updated, without architectural modification or inference overhead. Results: Evaluated on WikiArt and Anita datasets, our method significantly suppresses style mimicry (reducing average style similarity by 87.3%), while preserving generation quality (FID improvement <0.8) and perceptual transparency. It offers artists an efficient, deployable copyright protection paradigm grounded in representational disentanglement within diffusion architectures.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The rapid advancement of generative models, particularly diffusion-based approaches, has inadvertently facilitated their potential for misuse. Such models enable malicious exploiters to replicate artistic styles that capture an artist's creative labor, personal vision, and years of dedication in an inexpensive manner. This has led to a rise in the need and exploration of methods for protecting artworks against style mimicry. Although generic diffusion models can easily mimic an artistic style, finetuning amplifies this capability, enabling the model to internalize and reproduce the style with higher fidelity and control. We hypothesize that certain cross-attention layers exhibit heightened sensitivity to artistic styles. Sensitivity is measured through activation strengths of attention layers in response to style and content representations, and assessing their correlations with features extracted from external models. Based on our findings, we introduce an efficient and lightweight protection strategy, StyleProtect, that achieves effective style defense against fine-tuned diffusion models by updating only selected cross-attention layers. Our experiments utilize a carefully curated artwork dataset based on WikiArt, comprising representative works from 30 artists known for their distinctive and influential styles and cartoon animations from the Anita dataset. The proposed method demonstrates promising performance in safeguarding unique styles of artworks and anime from malicious diffusion customization, while maintaining competitive imperceptibility.

Problem

• Protecting artistic styles from imitation by fine-tuned diffusion models
• Identifying sensitive cross-attention layers responsible for style replication
• Developing lightweight defense against malicious style customization attacks

Innovation

• Protection via selected cross-attention layers update
• Defense against fine-tuned diffusion model mimicry
• Lightweight strategy safeguarding artistic style identity