The unprecedented capture and application of face images raise increasing
concerns on anonymization to fight against privacy disclosure. Most existing
methods may suffer from the problem of excessive change of the
identity-independent information or insufficient identity protection. In this
paper, we present a new face anonymization approach by distracting the
intrinsic and extrinsic identity attentions. On the one hand, we anonymize the
identity information in the feature space by distracting the intrinsic identity
attention. On the other, we anonymize the visual clues (i.e. appearance and
geometry structure) by distracting the extrinsic identity attention. Our
approach allows for flexible and intuitive manipulation of face appearance and
geometry structure to produce diverse results, and it can also be used to
instruct users to perform personalized anonymization. We conduct extensive
experiments on multiple datasets and demonstrate that our approach outperforms
state-of-the-art methods.

通过分散内在和外在身份关注，我们提出一种新的面部匿名化方法，能够灵活直观地操作面部外貌和几何结构以产生多样化的结果，并可以用于指导用户执行个性化的匿名化。我们在多个数据集上进行了广泛实验，并证明我们的方法优于现有的方法。

面部身份匿名化：通过内在和外在注意力扰动

Facial Identity Anonymization via Intrinsic and Extrinsic Attention  Distraction

Deep learning classifiers are crucial in the age of artificial intelligence.
The device-edge-based collaborative inference has been widely adopted as an
efficient framework for promoting its applications in IoT and 5G/6G networks.
However, it suffers from accuracy degradation under non-i.i.d. data
distribution and privacy disclosure. For accuracy degradation, direct use of
transfer learning and split learning is high cost and privacy issues remain.
For privacy disclosure, cryptography-based approaches lead to a huge overhead.
Other lightweight methods assume that the ground truth is non-sensitive and can
be exposed. But for many applications, the ground truth is the user's crucial
privacy-sensitive information. In this paper, we propose a framework of
Roulette, which is a task-oriented semantic privacy-preserving collaborative
inference framework for deep learning classifiers. More than input data, we
treat the ground truth of the data as private information. We develop a novel
paradigm of split learning where the back-end DNN is frozen and the front-end
DNN is retrained to be both a feature extractor and an encryptor. Moreover, we
provide a differential privacy guarantee and analyze the hardness of ground
truth inference attacks. To validate the proposed Roulette, we conduct
extensive performance evaluations using realistic datasets, which demonstrate
that Roulette can effectively defend against various attacks and meanwhile
achieve good model accuracy. In a situation where the non-i.i.d. is very
severe, Roulette improves the inference accuracy by 21\% averaged over
benchmarks, while making the accuracy of discrimination attacks almost
equivalent to random guessing.

本文介绍了一种针对深度学习分类器的任务导向语义隐私保护的协作推断框架 Roulette，通过使用分割学习、加密机制和差分隐私保证等技术，有效提高推断准确性并保护用户的隐私信息。