TIIS (한국인터넷정보학회)
Current Result Document : 73 / 74
| 한글제목(Korean Title) |
Data Correction For Enhancing Classification Accuracy By Unknown Deep Neural Network Classifiers |
| 영문제목(English Title) |
Data Correction For Enhancing Classification Accuracy By Unknown Deep Neural Network Classifiers |
| 저자(Author) |
Hyun Kwon
Hyunsoo Yoon
Daeseon Choi
|
| 원문수록처(Citation) |
VOL 15 NO. 09 PP. 3243 ~ 3257 (2021. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
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. |
| 키워드(Keyword) |
Data correction
deep neural network
Ensemble Method
Machine Learning
Poisoning attack
|
| 파일첨부 |
PDF 다운로드
|
