Text-to-image diffusion models, such as Stable Diffusion, generate highly
realistic images from text descriptions. However, the generation of certain
content at such high quality raises concerns. A prominent issue is the accurate
depiction of identifiable facial images, which could lead to malicious deepfake
generation and privacy violations. In this paper, we propose Anonymization
Prompt Learning (APL) to address this problem. Specifically, we train a
learnable prompt prefix for text-to-image diffusion models, which forces the
model to generate anonymized facial identities, even when prompted to produce
images of specific individuals. Extensive quantitative and qualitative
experiments demonstrate the successful anonymization performance of APL, which
anonymizes any specific individuals without compromising the quality of
non-identity-specific image generation. Furthermore, we reveal the
plug-and-play property of the learned prompt prefix, enabling its effective
application across different pretrained text-to-image models for transferrable
privacy and security protection against the risks of deepfakes.

通过使用可学习的提示前缀来训练文本到图像扩散模型，我们提出了 Anonymization Prompt Learning (APL) 来解决生成可识别面部图像的匿名化问题，实验证明了其成功的匿名化性能，并揭示了其在不同预训练文本到图像模型中的可插拔性质，以提供对抗深伪危险的可迁移隐私和安全保护。