Data Correction For Enhancing Classification Accuracy By Unknown Deep Neural Network Classifiers

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Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition. It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services. In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier. This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data. It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance. We tested our method using MNIST and CIFAR-10 as experimental data. The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data's recognizability by humans.
Publisher
KSII-KOR SOC INTERNET INFORMATION
Issue Date
2021-09
Language
English
Article Type
Article
Citation

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.15, no.9, pp.3243 - 3257

ISSN
1976-7277
DOI
10.3837/tiis.2021.09.009
URI
http://hdl.handle.net/10203/288476
Appears in Collection
CS-Journal Papers(저널논문)
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