# Block-wise Scrambled Image Recognition Using Adaptation Network

Source: [Block-wise Scrambled Image Recognition Using Adaptation Network](<https://www.sota2.com/research/paper/68fa66ea1e209131cea13260>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:02:19.923Z

Authors: Koki Madono, Masayuki Tanaka, Masaki Onishi, Tetsuji Ogawa

Published: 2020-01-21T20:22:10.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.07761>)

[PDF](<https://arxiv.org/pdf/2001.07761.pdf>)