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.

Related code

[Python code of image scramble]
[Matlab code of image scramble]

Reference

  1. 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.
    [Reproduction code] [arXiv]
  2. Masayuki Tanaka, Learnable Image Encryption, IEEE International Conference on Consumer Electronics TAIWAN (ICCE-TW), 2018.
    [Reproduction code][arXiv]
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