INSPIRE: Instance-Level Privacy-Pre Serving Transformation for Vehicular Camera Videos

Zhouyu Li, Ruozhou Yu, Anupam Das, Shaohu Zhang, Huayue Gu, Xiaojian Wang, Fangtong Zhou, Aafaq Sabir, Dilawer Ahmed, Ahsan Zafar
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Abstract

The wide spread of vehicular cameras has raised broad privacy concerns. Ubiquitous vehicular cameras capture bystanders like people or cars nearby without their awareness. To address privacy concerns, most existing works either blur out direct identifiers such as vehicle license plates and human faces, or obfuscate whole video frames. However, the former solution is vulnerable to re-identification attacks based on general features, and the latter severely impacts utility of the transformed videos. In this paper, we propose an INStance-level PrIvacy-pREserving (INSPIRE) video transformation framework for vehicular camera videos. INSPIRE leverages deep neural network models to detect and replace sensitive object instances in vehicular videos with their non-existent counterparts. We design INSPIRE as a modular framework to enable flexible customization of protected instance categories and their protection modules. An implementation of INSPIRE focused on protecting people and cars is described, which we tested on six re-identification datasets and three real-world vehicular video datasets to evaluate its privacy protection and utility preservation capability. Results show that INSPIRE can thwart 97% of re-identification attacks for people and cars while maintaining a 0.75 object detection mean average precision on transformed instances. We also demonstrate experimentally that INSPIRE is robust against model inversion attacks. Compared to solutions that provide comparable privacy protection, INSPIRE achieves relatively 1.76 times higher counting accuracy and 31.61% higher object detection mean average precision.
INSPIRE:车载摄像头视频的实例级隐私预服务转换
车载摄像头的广泛使用引发了广泛的隐私担忧。无处不在的车载摄像头在他们不知情的情况下捕捉到附近的行人或汽车。为了解决隐私问题,大多数现有的作品要么模糊了直接标识符,如车牌和人脸,要么模糊了整个视频帧。然而,前者容易受到基于一般特征的再识别攻击,后者严重影响转换后视频的实用性。本文提出了一种基于实例级隐私保护(INSPIRE)的车载摄像头视频变换框架。INSPIRE利用深度神经网络模型来检测和替换车载视频中不存在的敏感对象实例。我们将INSPIRE设计为一个模块化框架,以实现受保护实例类别及其保护模块的灵活定制。介绍了一种以保护人和车为重点的INSPIRE实现,我们在6个重新识别数据集和3个真实车辆视频数据集上对其进行了测试,以评估其隐私保护和效用保存能力。结果表明,INSPIRE可以挫败97%的人和车的再识别攻击,同时在转换实例上保持0.75的目标检测平均精度。我们还通过实验证明了INSPIRE对模型反演攻击的鲁棒性。与提供类似隐私保护的解决方案相比,INSPIRE的计数精度提高了1.76倍,目标检测平均精度提高了31.61%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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