Learnable Image Scramble
Block-wise Scrambled Image Recognition Using Adaptation Network
In this study, a perceptually hidden object-recognition
method is investigated to generate secure images recognizable
by humans but not machines. Hence, both the perceptual
information hiding and the corresponding object recognition
methods should be developed. Block-wise image scrambling
is introduced to hide perceptual information from a third
party. In addition, an adaptation network is proposed to recognize
those scrambled images. Experimental comparisons conducted
using CIFAR datasets demonstrated that the proposed
adaptation network performed well in incorporating simple
perceptual information hiding into DNN-based image classification.
Learnable Image Encryption
The network-based machine learning algorithm is very powerful tools. However, it requires huge training dataset. Researchers often meet privacy issues when they collect image dataset especially for surveillance applications. A learnable image encryption scheme is introduced. The key idea of this scheme is to encrypt images, so that human cannot understand images but the network can be train with encrypted images. This scheme allows us to train the network without the privacy issues.
[Python code of image scramble]
[Matlab code of image scramble]
Koki Madono, Masayuki Tanaka, Masaki Onishi, Tetsuji Ogawa, Block-wise Scrambled Image Recognition Using Adaptation Network, Artificial Intelligence of Things (AIoT), Workshop on AAAI conference Artificial Intellignece, (AAAI-WS), 2020.
Masayuki Tanaka, Learnable Image Encryption, IEEE International Conference on Consumer Electronics TAIWAN (ICCE-TW), 2018.
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