论文标题
部分可观测时空混沌系统的无模型预测
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning
论文作者
论文摘要
在智能城市部署的智能传感器，设备和系统为公民带来了改进的身体保护。通过执行运动检测，威胁和参与者分析以及实时警报的这些技术，可以实现预防犯罪的增强，以及消防和生命安全保护。但是，在这些日益普遍的部署中，重要的要求是保存隐私和保护个人可识别信息。因此，应将强大的加密和匿名技术应用于收集的数据。在IEEE大数据杯2022挑战中，将不同的掩蔽，编码和同型加密技术应用于图像以保护其内容的隐私。参与者必须开发检测解决方案，以执行这些图像的匹配的隐私匹配。在本文中，我们描述了基于最先进的深层卷积神经网络和各种数据增强技术的解决方案。我们的解决方案在2022年IEEE大数据杯中获得了第一名：保护加密图像挑战的隐私匹配。
Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data Cup 2022 challenge, different masking, encoding and homomorphic encryption techniques were applied to the images to protect the privacy of their contents. Participants are required to develop detection solutions to perform privacy preserving matching of these images. In this paper, we describe our solution which is based on state-of-the-art deep convolutional neural networks and various data augmentation techniques. Our solution achieved 1st place at the IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images Challenge.
