Authors: Songrui Wang, Yubo Zhu, Wei Tong, Sheng Zhong

Published on: September 27, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2409.18897

Summary

• What is new: Introducing a watermarking framework to detect and trace unauthorized use of datasets in text-to-image synthesis models.
• Why this is important: Unauthorized usage and sharing of valuable datasets during the fine-tuning of text-to-image synthesis models.
• What the research proposes: A dataset watermarking framework that employs two key strategies across multiple schemes to detect and trace unauthorized usage.
• Results: The framework is highly effective, requiring modification of only 2% of the data for high detection accuracy, and proves robust and transferable through extensive experiments.

Technical Details

Technological frameworks used: A dataset watermarking framework with multiple watermarking schemes.

Models used: Stable Diffusion models for text-to-image synthesis.

Data used: Visual datasets commonly used for text-to-image synthesis, watermarked to detect unauthorized usage.

Potential Impact

Companies and platforms offering AI-generated images, data-driven visual content creation, and digital rights management solutions could benefit from this framework.

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