Fixing the Double Agent Vulnerability of Deep Watermarking: A Patch-Level Solution against Artwork Plagiarism

• Published in: IEEE transactions on circuits and systems for video technology Vol. 34; no. 3; p. 1
• Main Authors: Luo, Yuanjing, Zhou, Tongqing, Cui, Shenglan, Ye, Yunfan, Liu, Fang, Cai, Zhiping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Art works; artwork copyright protection; convolutional neural networks; Copy protection; Copyright protection; Decoding; Deep watermarking; Distortion; Embedding; Encoders-Decoders; Plagiarism; plagiarism resistance; Robustness; Training; Watermarking
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2023.3295895

Increasing artwork plagiarism incidents stresses the urgent need for proper Copyright protection on behalf of the creators. The latest development in this context focuses on embedding watermarks via deep encoder-decoder networks. However, we find that deep watermarking has a serious vulnerability on its robustness when facing deliberate plagiarism. To manifest it, we construct an attack that misuses watermarking encoder as a plagiarism lookout for bypassing copyright detection. As a remedy, we propose a patch-level deep watermarking framework (DIPW) to retain copyright evidence in essential patches with plagiarism resistance, inspired by a user study observation that subject elements in artworks are the principal plagiarism entities. Technically, DIPW adaptively finds the embedding patches by identifying a subset of non-overlapping and feature-rich objects; and tailors the model with dual-distortion losses and adversarial plagiarism noise injection for robustness. Experimental results demonstrate the superiority of DIPW in facilitating better robustness, secrecy, and imperceptibility with acceptable time burden.