Image to Perturbation: An Image Transformation Network for Generating Visually Protected Images for Privacy-Preserving Deep Neural Networks

Image to Perturbation: An Image Transformation Network for Generating Visually Protected Images for Privacy-Preserving Deep Neural Networks

We propose a novel image transformation network for generating visually protected images for privacy-preserving deep neural networks (DNNs). The proposed transformation network is trained by using a plain image dataset so that plain images are converted into visually protected ones. Conventional per...

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Bibliographic Details
Main Authors: Hiroki Ito, Yuma Kinoshita, Maungmaung Aprilpyone, Hitoshi Kiya
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Adversarial example
deep neural network
privacy preserving
visual protection
Online Access:https://ieeexplore.ieee.org/document/9410466/

Internet

https://ieeexplore.ieee.org/document/9410466/

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