论文标题
关于隐私感知相机的设计：深神经网络的研究
On the Design of Privacy-Aware Cameras: a Study on Deep Neural Networks
论文作者
论文摘要
尽管个人数据保护方面的法律进展，但未经授权实体滥用的私人数据问题仍然至关重要。为了防止这种情况，通常提出设计隐私作为数据保护的解决方案。在本文中，使用通常用于提取敏感数据的深度学习技术来研究摄像机扭曲的效果。为此，我们模拟了与具有固定焦距，光圈和焦点的现实摄像机以及来自单色相机的灰度图像相对应的焦点外图像。然后，我们通过一项实验研究证明，我们可以构建一个无法提取个人信息（例如车牌编号）的隐私相机。同时，我们确保仍然可以从变形的图像中提取有用的非敏感数据。代码可在https://github.com/upciti/privacy-by-design-semseg上找到。
In spite of the legal advances in personal data protection, the issue of private data being misused by unauthorized entities is still of utmost importance. To prevent this, Privacy by Design is often proposed as a solution for data protection. In this paper, the effect of camera distortions is studied using Deep Learning techniques commonly used to extract sensitive data. To do so, we simulate out-of-focus images corresponding to a realistic conventional camera with fixed focal length, aperture, and focus, as well as grayscale images coming from a monochrome camera. We then prove, through an experimental study, that we can build a privacy-aware camera that cannot extract personal information such as license plate numbers. At the same time, we ensure that useful non-sensitive data can still be extracted from distorted images. Code is available at https://github.com/upciti/privacy-by-design-semseg .
