Your Text Encoder Can Be An Object-Level Watermarking Controller
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912278341746688 |
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| author | Devulapally, Naresh Kumar Huang, Mingzhen Asnani, Vishal Agarwal, Shruti Lyu, Siwei Lokhande, Vishnu Suresh |
| author_facet | Devulapally, Naresh Kumar Huang, Mingzhen Asnani, Vishal Agarwal, Shruti Lyu, Siwei Lokhande, Vishnu Suresh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Your Text Encoder Can Be An Object-Level Watermarking Controller Devulapally, Naresh Kumar Huang, Mingzhen Asnani, Vishal Agarwal, Shruti Lyu, Siwei Lokhande, Vishnu Suresh Computer Vision and Pattern Recognition Cryptography and Security Machine Learning 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. |
| title | Your Text Encoder Can Be An Object-Level Watermarking Controller |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2503.11945 |