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.

本文提出了一种新的数字水印系统 GenWatermark, 该系统结合了数字水印生产者和检测器，通过 fine-tuning 与合成的图像一起学习水印，以保护被合成图像的主体权益。实验证明，GenWatermark 对未知模型和文本提示以及部分数据水印化的有效性较高，并且对抵抗合成质量的潜在对策具有鲁棒性。