Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2025
Abstract
Image generation technology has brought significant advancements across various fields but has also raised concerns about data misuse and potential rights infringements, particularly with respect to creating visual artworks. Current methods aimed at safeguarding artworks often employ adversarial attacks. However, these methods face challenges such as poor transferability, high computational costs, and the introduction of noticeable noise, which compromises the aesthetic quality of the original artwork. To address these limitations, we propose a Structurally Imperceptible and Transferable Adversarial (SITA) attacks. SITA leverages a CLIP-based destylization loss, which decouples and disrupts the robust style representation of the image. This disruption hinders style extraction during stylized image generation, thereby impairing the overall stylization process. Importantly, SITA eliminates the need for a surrogate diffusion model, leading to significantly reduced computational overhead. The method’s robust style feature disruption ensures high transferability across diverse models. Moreover, SITA introduces perturbations by embedding noise within the imperceptible structural details of the image. This approach effectively protects against style extraction without compromising the visual quality of the artwork. Extensive experiments demonstrate that SITA offers superior protection for artworks against unauthorized use in stylized generation. It significantly outperforms existing methods in terms of transferability, computational efficiency, and noise imperceptibility. Code is available at https://github.com/A-raniy-day/SITA.
Discipline
Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Information Forensics and Security
Volume
20
First Page
3936
Last Page
3949
ISSN
1556-6013
Identifier
10.1109/TIFS.2025.3555552
Publisher
Institute of Electrical and Electronics Engineers
Citation
KANG, Jingdan; YANG, Haoxin; CAI, Yan; ZHANG, Huaidong; XU, Xuemiao; DU, Yong; and HE, Shengfeng.
SITA: Structurally imperceptible and transferable adversarial attacks for stylized image generation. (2025). IEEE Transactions on Information Forensics and Security. 20, 3936-3949.
Available at: https://ink.library.smu.edu.sg/sis_research/10608
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TIFS.2025.3555552