Open Access

ARTICLE

ArtFlow: Flow-Based Watermarking for High-Quality Artwork Images Protection

Yuanjing Luo^1,2,#, Xichen Tan^1,#, Yinuo Jiang¹, Zhiping Cai^1,*

1 College of Computer Science and Technology, National University of Defense Technology, Changsha, China
2 College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, China

* Corresponding Author: Zhiping Cai. Email:
# These authors contributed equally to this work

Computers, Materials & Continua 2026, 88(1), 38 https://doi.org/10.32604/cmc.2026.077803

Received 17 December 2025; Accepted 18 March 2026; Issue published 08 May 2026

Abstract

With increasing artwork plagiarism incidents, the necessity of using digital watermarking technology for high-quality artwork copyright protection is evident. Current digital watermarking methods are limited in imperceptibility and robustness. To address this, based on comprehensive copyright protection research, we develop a novel watermark framework named ArtFlow, using Invertible Neural Networks (INN). Our framework treats watermark embedding and recovery as inverse image transformations, implemented through forward and reverse processes of INN. To ensure high-quality watermark embedding, we utilize frequency domain transformations and attention mechanisms to guide the watermark into high-frequency areas of the image that have greater protective weighting. These areas are attractive to plagiarizers yet have minimal impact on the artistic integrity of the artwork itself. For strong plagiarism-resistant, we design a noise layer that includes various infringement methods—transmission, plagiarism action, and camera-shooting—to train robust watermark recovery process. Additionally, an image quality enhancement module is introduced to minimize the distortions that may arise from infringement before the watermark recovery. Experimental results across four datasets confirm that our ArtFlow surpasses existing advanced watermarking methods.

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Keywords

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