FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge

• URL: http://arxiv.org/abs/2504.03128v1
• Date: Fri, 04 Apr 2025 02:39:33 GMT
• Title: FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge
• Authors: Kahim Wong, Jicheng Zhou, Kemou Li, Yain-Whar Si, Xiaowei Wu, Jiantao Zhou,
• Abstract summary: We introduce FontGuard, a novel font watermarking model that harnesses the capabilities of font models and language-guided contrastive learning.<n>FontGuard modifies fonts by altering hidden style features, resulting in better font quality upon watermark embedding.<n>In the decoder, we employ an image-text contrastive learning to reconstruct the embedded bits, which can achieve desirable robustness against various real-world transmission distortions.
• Score: 14.545769739571291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of AI-generated content brings significant concerns on the forensic and security issues such as source tracing, copyright protection, etc, highlighting the need for effective watermarking technologies. Font-based text watermarking has emerged as an effective solution to embed information, which could ensure copyright, traceability, and compliance of the generated text content. Existing font watermarking methods usually neglect essential font knowledge, which leads to watermarked fonts of low quality and limited embedding capacity. These methods are also vulnerable to real-world distortions, low-resolution fonts, and inaccurate character segmentation. In this paper, we introduce FontGuard, a novel font watermarking model that harnesses the capabilities of font models and language-guided contrastive learning. Unlike previous methods that focus solely on the pixel-level alteration, FontGuard modifies fonts by altering hidden style features, resulting in better font quality upon watermark embedding. We also leverage the font manifold to increase the embedding capacity of our proposed method by generating substantial font variants closely resembling the original font. Furthermore, in the decoder, we employ an image-text contrastive learning to reconstruct the embedded bits, which can achieve desirable robustness against various real-world transmission distortions. FontGuard outperforms state-of-the-art methods by +5.4%, +7.4%, and +5.8% in decoding accuracy under synthetic, cross-media, and online social network distortions, respectively, while improving the visual quality by 52.7% in terms of LPIPS. Moreover, FontGuard uniquely allows the generation of watermarked fonts for unseen fonts without re-training the network. The code and dataset are available at https://github.com/KAHIMWONG/FontGuard.

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