论文标题
无眼分类的增强隐私的光学嵌入
Privacy-Enhancing Optical Embeddings for Lensless Classification
论文作者
论文摘要
由于其测量的高度多路复用特征，无透镜成像可以提供视觉隐私。但是，仅这是一种薄弱的安全形式，因为可以设计各种对抗性攻击来颠倒此类摄像机的一对多场景映射。在这项工作中，我们通过（1）在传感器对（1）下采样和（2）使用具有可变模式作为光学编码器的可编程掩码来增强无透镜成像提供的隐私。我们从低成本LCD和Raspberry Pi组件建立了原型，总成本约为100美元。这个非常低的价格可以使我们的系统在广泛的应用程序中部署和利用。在我们的实验中，我们首先通过将系统应用于各种分类任务：MNIST，CELEBA（face属性）和CIFAR10来证明系统的可行性和可重新配置性。通过以端到端的方式共同优化蒙版模式和数字分类器，直接在传感器上直接学习了低维，增强隐私的嵌入。其次，我们通过可变掩码模式来展示拟议的系统如何通过违反系统（1）通过明文攻击或（2）摄像机参数泄漏而试图挫败系统（1）的对手。我们证明了我们对这两种风险的防御措施，基于基于模型的凸优化和生成神经网络的攻击，图像质量指标的图像质量指标下降了55％和26％。我们为端到端优化所需的波浪传播和相机模拟器开放，训练软件和用于与相机接口的库。
Lensless imaging can provide visual privacy due to the highly multiplexed characteristic of its measurements. However, this alone is a weak form of security, as various adversarial attacks can be designed to invert the one-to-many scene mapping of such cameras. In this work, we enhance the privacy provided by lensless imaging by (1) downsampling at the sensor and (2) using a programmable mask with variable patterns as our optical encoder. We build a prototype from a low-cost LCD and Raspberry Pi components, for a total cost of around 100 USD. This very low price point allows our system to be deployed and leveraged in a broad range of applications. In our experiments, we first demonstrate the viability and reconfigurability of our system by applying it to various classification tasks: MNIST, CelebA (face attributes), and CIFAR10. By jointly optimizing the mask pattern and a digital classifier in an end-to-end fashion, low-dimensional, privacy-enhancing embeddings are learned directly at the sensor. Secondly, we show how the proposed system, through variable mask patterns, can thwart adversaries that attempt to invert the system (1) via plaintext attacks or (2) in the event of camera parameters leaks. We demonstrate the defense of our system to both risks, with 55% and 26% drops in image quality metrics for attacks based on model-based convex optimization and generative neural networks respectively. We open-source a wave propagation and camera simulator needed for end-to-end optimization, the training software, and a library for interfacing with the camera.
