薛定谔的相机：迈向基于量子的隐私保护相机的第一步
Schrödinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera
AI总结：
本文提出了一种基于量子态的隐私保护图像存储方法，并利用双深度Q学习算法实现对图像匿名化的控制，展示了在量子基础上平衡隐私与效用的可行性。
摘要：
隐私保护的视觉必须克服效用和隐私的双重挑战。过多的匿名性使图像变得无用，但过少的隐私无法保护敏感数据。我们提出了一种新的隐私保护设计，其中图像存储在量子态中。未来，这将通过量子成像相机实现，目前可以将非常低分辨率的图像存储在量子态中。量子态图像的优势在于在测量点之前既是私人的又是非私人的。即使图像被篡改，由于每次量子操作都是完全可逆的，这一点仍然成立。我们提出了一种基于双深度Q学习的控制算法，用于在测量前学习如何匿名化图像。学习后，RL权重固定，新的攻击神经网络从头开始训练以打破系统的隐私。尽管所有结果都在模拟中，我们通过这些初步步骤展示了在量子基础上控制隐私和效用的可能性。
英文摘要：
Privacy-preserving vision must overcome the dual challenge of utility and privacy. Too much anonymity renders the images useless, but too little privacy does not protect sensitive data. We propose a novel design for privacy preservation, where the imagery is stored in quantum states. In the future, this will be enabled by quantum imaging cameras, and, currently, storing very low resolution imagery in quantum states is possible. Quantum state imagery has the advantage of being both private and non-private till the point of measurement. This occurs even when images are manipulated, since every quantum action is fully reversible. We propose a control algorithm, based on double deep Q-learning, to learn how to anonymize the image before measurement. After learning, the RL weights are fixed, and new attack neural networks are trained from scratch to break the system's privacy. Although all our results are in simulation, we demonstrate, with these first steps, that it is possible to control both privacy and utility in a quantum-based manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
