Your Text Encoder Can Be An Object-Level Watermarking Controller

• URL: http://arxiv.org/abs/2503.11945v1
• Date: Sat, 15 Mar 2025 01:35:39 GMT
• Title: Your Text Encoder Can Be An Object-Level Watermarking Controller
• Authors: Naresh Kumar Devulapally, Mingzhen Huang, Vishal Asnani, Shruti Agarwal, Siwei Lyu, Vishnu Suresh Lokhande,
• Abstract summary: We present a novel approach to watermark images of T2I Latent Diffusion Models (LDMs)<n>By only fine-tuning text token embeddings $W_*$, we enable watermarking in selected objects or parts of the image, offering greater flexibility compared to traditional full-image watermarking.<n>Our approach achieves $99%$ bit accuracy ($48$ bits) with a $105 times$ reduction in model parameters, enabling efficient watermarking.
• Score: 31.003510691494473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Invisible watermarking of AI-generated images can help with copyright protection, enabling detection and identification of AI-generated media. In this work, we present a novel approach to watermark images of T2I Latent Diffusion Models (LDMs). By only fine-tuning text token embeddings $W_*$, we enable watermarking in selected objects or parts of the image, offering greater flexibility compared to traditional full-image watermarking. Our method leverages the text encoder's compatibility across various LDMs, allowing plug-and-play integration for different LDMs. Moreover, introducing the watermark early in the encoding stage improves robustness to adversarial perturbations in later stages of the pipeline. Our approach achieves $99\%$ bit accuracy ($48$ bits) with a $10^5 \times$ reduction in model parameters, enabling efficient watermarking.

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