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Blur vs. Block: Investigating the Effectiveness of Privacy-Enhancing Obfuscation for Images

机译:模糊与遮挡:研究图像增强隐私混淆的有效性

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Computer vision can lead to privacy issues such as unauthorized disclosure of private information and identity theft, but it may also be used to preserve user privacy. For example, using computer vision, we may be able to identify sensitive elements of an image and obfuscate those elements thereby protecting private information or identity. However, there is a lack of research investigating the effectiveness of applying obfuscation techniques to parts of images as a privacy enhancing technology. In particular, we know very little about how well obfuscation works for human viewers or users' attitudes towards using these mechanisms. In this paper, we report results from an online experiment with 53 participants that investigates the effectiveness two exemplar obfuscation techniques: "blurring" and "blocking", and explores users' perceptions of these obfuscations in terms of image satisfaction, information sufficiency, enjoyment, and social presence. Results show that although "blocking" is more effective at de-identification compared to "blurring" or leaving the image "as is", users' attitudes towards "blocking" are the most negative, which creates a conflict between privacy protection and users' experience. Future work should explore alternative obfuscation techniques that could protect users' privacy and also provide a good viewing experience.
机译:计算机视觉可以导致隐私问题,例如未经授权的私人信息泄露和身份盗用,但是它也可以用于保护用户隐私。例如,使用计算机视觉,我们可能能够识别图像的敏感元素并混淆这些元素,从而保护私人信息或身份。然而,缺乏研究将模糊处理技术应用于图像的一部分作为隐私增强技术的有效性的研究。尤其是,我们对混淆对于人类观众或用户使用这些机制的态度的效果知之甚少。在本文中,我们报告了一个在线实验的结果,该实验由53位参与者组成,该实验研究了两种示例性混淆技术(“模糊”和“遮挡”)的有效性,并从图像满意度,信息充足性,娱乐性,和社会存在。结果表明,尽管与“模糊处理”或“按原样”保留图像相比,“阻止”在识别时更有效,但用户对“阻止”的态度最为消极,这在隐私保护与用户隐私之间产生了冲突。经验。未来的工作应该探索替代的混淆技术,这些技术可以保护用户的隐私并提供良好的观看体验。

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