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。

面部匿名提示学习用于保护隐私的文本到图像生成

Anonymization Prompt Learning for Facial Privacy-Preserving  Text-to-Image Generation

Equivariance to random image transformations is an effective method to learn
landmarks of object categories, such as the eyes and the nose in faces, without
manual supervision. However, this method does not explicitly guarantee that the
learned landmarks are consistent with changes between different instances of
the same object, such as different facial identities. In this paper, we develop
a new perspective on the equivariance approach by noting that dense landmark
detectors can be interpreted as local image descriptors equipped with
invariance to intra-category variations. We then propose a direct method to
enforce such an invariance in the standard equivariant loss. We do so by
exchanging descriptor vectors between images of different object instances
prior to matching them geometrically. In this manner, the same vectors must
work regardless of the specific object identity considered. We use this
approach to learn vectors that can simultaneously be interpreted as local
descriptors and dense landmarks, combining the advantages of both. Experiments
on standard benchmarks show that this approach can match, and in some cases
surpass state-of-the-art performance amongst existing methods that learn
landmarks without supervision. Code is available at
www.robots.ox.ac.uk/~vgg/research/DVE/.

本论文研究对象类别的标志物，提出使用等变性法则与局部图像描述符相结合的方法，使得不同对象实例之间学习到的标记物是具有一致性的。实验表明该方法能够超越现有无监督学习方法的表现。