SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images

TL;DR

This paper introduces a novel adversarial example technique called SocialGuard that effectively prevents object detectors from identifying sensitive objects in social images, thereby enhancing privacy without degrading image quality.

Contribution

The paper presents an Object Disappearance Algorithm that creates adversarial perturbations to hide or misclassify objects in social images, outperforming existing image processing methods.

Findings

Achieves privacy-preserving success rates up to 99.3% on benchmark datasets.

Maintains high visual quality of images after applying adversarial perturbations.

Significantly reduces privacy leakage rates compared to traditional image processing techniques.

Abstract

The popularity of various social platforms has prompted more people to share their routine photos online. However, undesirable privacy leakages occur due to such online photo sharing behaviors. Advanced deep neural network (DNN) based object detectors can easily steal users' personal information exposed in shared photos. In this paper, we propose a novel adversarial example based privacy-preserving technique for social images against object detectors based privacy stealing. Specifically, we develop an Object Disappearance Algorithm to craft two kinds of adversarial social images. One can hide all objects in the social images from being detected by an object detector, and the other can make the customized sensitive objects be incorrectly classified by the object detector. The Object Disappearance Algorithm constructs perturbation on a clean social image. After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded. We use two metrics, privacy-preserving success rate and privacy leakage rate, to evaluate the effectiveness of the proposed method. Experimental results show that, the proposed method can effectively protect the privacy of social images. The privacy-preserving success rates of the proposed method on MS-COCO and PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the privacy leakage rates on these two datasets are as low as 0.57% and 0.07%, respectively. In addition, compared with existing image processing methods (low brightness, noise, blur, mosaic and JPEG compression), the proposed method can achieve much better performance in privacy protection and image visual quality maintenance.