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Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images

2023-03-18 15:23:10
Yuntao Wang, Zirui Cheng, Xin Yi, Yan Kong, Xueyang Wang, Xuhai Xu, Yukang Yan, Chun Yu, Shwetak Patel, Yuanchun Shi

Abstract

A computer vision system using low-resolution image sensors can provide intelligent services (e.g., activity recognition) but preserve unnecessary visual privacy information from the hardware level. However, preserving visual privacy and enabling accurate machine recognition have adversarial needs on image resolution. Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors. In this paper, using the at-home activity of daily livings (ADLs) as the scenario, we first obtained the most important visual privacy features through a user survey. Then we quantified and analyzed the effects of image resolution on human and machine recognition performance in activity recognition and privacy awareness tasks. We also investigated how modern image super-resolution techniques influence these effects. Based on the results, we proposed a method for modeling the trade-off of privacy preservation and activity recognition on low-resolution images.

Abstract (translated)

使用低分辨率图像传感器的计算机视觉系统可以提供智能服务(例如,活动识别),但从硬件级别上保留不必要的视觉隐私信息。然而,保护视觉隐私并实现准确的机器识别具有对图像分辨率的dversarial需求。通过建模隐私保护和机器识别性能之间的权衡,可以指导未来使用低分辨率图像传感器的保持隐私的计算机视觉系统。在本文中,使用家庭日常活动(ADLs)作为场景,通过用户调查获取了最重要的视觉隐私特征。然后量化和分析了图像分辨率对活动识别和隐私意识任务中人类和机器识别性能的影响。我们还研究了现代图像超分辨率技术如何影响这些影响。基于结果,我们提出了一种方法,用于建模低分辨率图像中隐私保护和活动识别的权衡。

URL

https://arxiv.org/abs/2303.10435

PDF

https://arxiv.org/pdf/2303.10435.pdf


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