作者：Zikui Cai Zhongpai Gao Benjamin Planche Meng Zheng Terrence Chen M. Salman Asif Ziyan Wu
随着摄像头和智能传感器的日益普及，人类正在以指数级的速度生成数据。访问这些信息宝库，通常涵盖但代表性不足的用例（例如，医疗环境中的人工智能），可以推动新一代的深度学习工具。然而，热心的数据科学家应该首先为这些未开发的数据集中的个人隐私提供令人满意的保证。这对于描绘人脸的图像或视频尤其重要，因为人脸的生物特征信息是大多数识别方法的目标。虽然已经提出了各种解决方案来消除对此类图像的识别，但它们往往会破坏与下游任务相关的其他非识别面部属性。在本文中，我们提出了Disguise，这是一种新的算法，可以无缝地去识别面部图像，同时确保更改数据的可用性。与现有技术不同，我们的解决方案立足于差异隐私和集成学习研究领域。
With the increasing ubiquity of cameras and smart sensors, humanity is generating data at an exponential rate. Access to this trove of information, often covering yet-underrepresented use-cases (e.g., AI in medical settings) could fuel a new generation of deep-learning tools. However, eager data scientists should first provide satisfying guarantees w.r.t. the privacy of individuals present in these untapped datasets. This is especially important for images or videos depicting faces, as their biometric information is the target of most identification methods. While a variety of solutions have been proposed to de-identify such images, they often corrupt other non-identifying facial attributes that would be relevant for downstream tasks. In this paper, we propose Disguise, a novel algorithm to seamlessly de-identify facial images while ensuring the usability of the altered data. Unlike prior arts, we ground our solution in both differential privacy and ensemble-learning research domains. Our method extracts and swaps depicted identities with fake ones, synthesized via variational mechanisms to maximize obfuscation and non-invertibility; while leveraging the supervision from a mixture-of-experts to disentangle and preserve other utility attributes. We extensively evaluate our method on multiple datasets, demonstrating higher de-identification rate and superior consistency than prior art w.r.t. various downstream tasks.
论文链接：http://arxiv.org/pdf/2303.13269v1
更多计算机论文：http://cspaper.cn/