PAG: Protecting Artworks from Personalizing Image Generative Models

Zhaorui Tan, Siyuan Wang, Xi Yang*, Kaizhu Huang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Recent advances in conditional image generation have led to powerful personalized generation models that generate high-resolution artistic images based on simple text descriptions through tuning. However, the abuse of personalized generation models may also increase the risk of plagiarism and the misuse of artists’ painting styles. In this paper, we propose a novel method called Protecting Artworks from Personalizing Image Generative Models framework (PAG) to safeguard artistic images from the malicious use of generative models. By injecting learned target perturbations into the original artistic images, we aim to disrupt the tuning process and introduce the distortions that protect the authenticity and integrity of the artist’s style. Furthermore, human evaluations suggest that our PAG model offers a feasible and effective way to protect artworks, preventing the personalized generation models from generating similar images to the given artworks.

Original languageEnglish
Title of host publicationNeural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
EditorsBiao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789819980697
Publication statusPublished - 2024
Event30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China
Duration: 20 Nov 202323 Nov 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14450 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Conference on Neural Information Processing, ICONIP 2023


  • Conditional image generation
  • Image protection
  • Personalizing generation models


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