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An Image Privacy Protection Algorithm Based on Adversarial Perturbation Generative Networks

Published:21 April 2021Publication History
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Abstract

Today, users of social platforms upload a large number of photos. These photos contain personal private information, including user identity information, which is easily gleaned by intelligent detection algorithms. To thwart this, in this work, we propose an intelligent algorithm to prevent deep neural network (DNN) detectors from detecting private information, especially human faces, while minimizing the impact on the visual quality of the image. More specifically, we design an image privacy protection algorithm by training and generating a corresponding adversarial sample for each image to defend DNN detectors. In addition, we propose an improved model based on the previous model by training an adversarial perturbation generative network to generate perturbation instead of training for each image. We evaluate and compare our proposed algorithm with other methods on wider face dataset and others by three indicators: Mean average precision, Averaged distortion, and Time spent. The results show that our method significantly interferes with DNN detectors while causing weak impact to the visual quality of images, and our improved model does speed up the generation of adversarial perturbations.

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        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 2
          May 2021
          410 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3461621
          Issue’s Table of Contents

          Copyright © 2021 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 21 April 2021
          • Online AM: 7 May 2020
          • Accepted: 1 January 2020
          • Revised: 1 December 2019
          • Received: 1 September 2019
          Published in tomm Volume 17, Issue 2

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