Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis Article Swipe
Yihan Ma
,
Zhengyu Zhao
,
Xinlei He
,
Zheng Li
,
Michael Backes
,
Yang Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07754
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07754
Large text-to-image models have shown remarkable performance in synthesizing high-quality images. In particular, the subject-driven model makes it possible to personalize the image synthesis for a specific subject, e.g., a human face or an artistic style, by fine-tuning the generic text-to-image model with a few images from that subject. Nevertheless, misuse of subject-driven image synthesis may violate the authority of subject owners. For example, malicious users may use subject-driven synthesis to mimic specific artistic styles or to create fake facial images without authorization. To protect subject owners against such misuse, recent attempts have commonly relied on adversarial examples to indiscriminately disrupt subject-driven image synthesis. However, this essentially prevents any benign use of subject-driven synthesis based on protected images. In this paper, we take a different angle and aim at protection without sacrificing the utility of protected images for general synthesis purposes. Specifically, we propose GenWatermark, a novel watermark system based on jointly learning a watermark generator and a detector. In particular, to help the watermark survive the subject-driven synthesis, we incorporate the synthesis process in learning GenWatermark by fine-tuning the detector with synthesized images for a specific subject. This operation is shown to largely improve the watermark detection accuracy and also ensure the uniqueness of the watermark for each individual subject. Extensive experiments validate the effectiveness of GenWatermark, especially in practical scenarios with unknown models and text prompts (74% Acc.), as well as partial data watermarking (80% Acc. for 1/4 watermarking). We also demonstrate the robustness of GenWatermark to two potential countermeasures that substantially degrade the synthesis quality.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.07754
- https://arxiv.org/pdf/2306.07754
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380714906
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380714906Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.07754Digital Object Identifier
- Title
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Generative Watermarking Against Unauthorized Subject-Driven Image SynthesisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-13Full publication date if available
- Authors
-
Yihan Ma, Zhengyu Zhao, Xinlei He, Zheng Li, Michael Backes, Yang ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.07754Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.07754Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2306.07754Direct OA link when available
- Concepts
-
Watermark, Subject (documents), Computer science, Digital watermarking, Artificial intelligence, Image (mathematics), Computer vision, Computer security, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.remarkable | 5 |
| abstract_inverted_index.robustness | 245 |
| abstract_inverted_index.synthesis, | 168 |
| abstract_inverted_index.synthesis. | 103 |
| abstract_inverted_index.uniqueness | 203 |
| abstract_inverted_index.adversarial | 96 |
| abstract_inverted_index.demonstrate | 243 |
| abstract_inverted_index.essentially | 106 |
| abstract_inverted_index.experiments | 212 |
| abstract_inverted_index.fine-tuning | 37, 178 |
| abstract_inverted_index.incorporate | 170 |
| abstract_inverted_index.particular, | 12, 160 |
| abstract_inverted_index.performance | 6 |
| abstract_inverted_index.personalize | 20 |
| abstract_inverted_index.sacrificing | 131 |
| abstract_inverted_index.synthesized | 182 |
| abstract_inverted_index.GenWatermark | 176, 247 |
| abstract_inverted_index.high-quality | 9 |
| abstract_inverted_index.synthesizing | 8 |
| abstract_inverted_index.watermarking | 235 |
| abstract_inverted_index.GenWatermark, | 144, 217 |
| abstract_inverted_index.Nevertheless, | 49 |
| abstract_inverted_index.Specifically, | 141 |
| abstract_inverted_index.effectiveness | 215 |
| abstract_inverted_index.substantially | 253 |
| abstract_inverted_index.text-to-image | 1, 40 |
| abstract_inverted_index.authorization. | 82 |
| abstract_inverted_index.subject-driven | 14, 52, 68, 101, 112, 167 |
| abstract_inverted_index.watermarking). | 240 |
| abstract_inverted_index.countermeasures | 251 |
| abstract_inverted_index.indiscriminately | 99 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |