Abstract
With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.
Highlights
The recent advances in multimedia recording devices, such as phones, cameras, drones, and other types of sensors, have greatly facilitated the collection of multimedia data, especially in the form of images and videos
We propose an image privacy protection framework that can protect the privacy in Internet of Multimedia Things (IoMT) images
We propose a new image privacy protection method based on the differential privacy method combined with generative adversarial networks (GANs)
Summary
The recent advances in multimedia recording devices, such as phones, cameras, drones, and other types of sensors, have greatly facilitated the collection of multimedia data, especially in the form of images and videos In such an era of IoMT, a massive number of images are widely used, by social network personal users and by government and companies. We use DP to control the generation of de-identify objects in images to mitigate privacy threats To overcome these obstacles, we propose a three-stage framework for image privacy protection in this paper.
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