The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
최근 몇 년 동안 딥러닝은 이미지 인식, 음성 처리 및 기타 연구 분야에서 탁월한 결과를 얻었으며, 이로 인해 연구 및 응용이 새로운 급증을 일으켰습니다. 내부 결함과 외부의 악의적인 공격은 딥러닝 시스템의 안전하고 안정적인 운영을 위협할 수 있으며, 심지어 감당하기 힘든 결과를 초래할 수도 있습니다. 딥러닝 시스템을 테스트하는 기술은 아직 초기 단계입니다. 기존 소프트웨어 테스트 기술은 딥 러닝 시스템 테스트에 적용할 수 없습니다. 또한, 복잡한 애플리케이션 시나리오, 입력 데이터의 높은 차원성, 연산 로직의 빈약한 해석성 등 딥러닝의 특성은 테스트 작업에 새로운 과제를 안겨줍니다. 본 논문은 테스트 케이스 생성의 문제에 초점을 맞추고, 적대적인 예제가 테스트 케이스로 사용될 수 있음을 지적합니다. 그런 다음 논문에서는 Generative Adversarial Network 기반의 딥러닝 이미지 분류기에 대한 테스트 케이스를 생성하기 위한 프레임워크인 MTGAN을 제안합니다. 마지막으로 본 논문에서는 MTGAN의 효율성을 평가한다.
Erhu LIU
Army Engineering University of PLA,94973 Troop, Hangzhou
Song HUANG
Army Engineering University of PLA
Cheng ZONG
Army Engineering University of PLA
Changyou ZHENG
Army Engineering University of PLA
Yongming YAO
Army Engineering University of PLA
Jing ZHU
Army Engineering University of PLA
Shiqi TANG
Army Engineering University of PLA
Yanqiu WANG
Baopo Technology Co. Ltd.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Erhu LIU, Song HUANG, Cheng ZONG, Changyou ZHENG, Yongming YAO, Jing ZHU, Shiqi TANG, Yanqiu WANG, "MTGAN: Extending Test Case set for Deep Learning Image Classifier" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 5, pp. 709-722, May 2021, doi: 10.1587/transinf.2020EDP7162.
Abstract: During the recent several years, deep learning has achieved excellent results in image recognition, voice processing, and other research areas, which has set off a new upsurge of research and application. Internal defects and external malicious attacks may threaten the safe and reliable operation of a deep learning system and even cause unbearable consequences. The technology of testing deep learning systems is still in its infancy. Traditional software testing technology is not applicable to test deep learning systems. In addition, the characteristics of deep learning such as complex application scenarios, the high dimensionality of input data, and poor interpretability of operation logic bring new challenges to the testing work. This paper focuses on the problem of test case generation and points out that adversarial examples can be used as test cases. Then the paper proposes MTGAN which is a framework to generate test cases for deep learning image classifiers based on Generative Adversarial Network. Finally, this paper evaluates the effectiveness of MTGAN.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7162/_p
부
@ARTICLE{e104-d_5_709,
author={Erhu LIU, Song HUANG, Cheng ZONG, Changyou ZHENG, Yongming YAO, Jing ZHU, Shiqi TANG, Yanqiu WANG, },
journal={IEICE TRANSACTIONS on Information},
title={MTGAN: Extending Test Case set for Deep Learning Image Classifier},
year={2021},
volume={E104-D},
number={5},
pages={709-722},
abstract={During the recent several years, deep learning has achieved excellent results in image recognition, voice processing, and other research areas, which has set off a new upsurge of research and application. Internal defects and external malicious attacks may threaten the safe and reliable operation of a deep learning system and even cause unbearable consequences. The technology of testing deep learning systems is still in its infancy. Traditional software testing technology is not applicable to test deep learning systems. In addition, the characteristics of deep learning such as complex application scenarios, the high dimensionality of input data, and poor interpretability of operation logic bring new challenges to the testing work. This paper focuses on the problem of test case generation and points out that adversarial examples can be used as test cases. Then the paper proposes MTGAN which is a framework to generate test cases for deep learning image classifiers based on Generative Adversarial Network. Finally, this paper evaluates the effectiveness of MTGAN.},
keywords={},
doi={10.1587/transinf.2020EDP7162},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - MTGAN: Extending Test Case set for Deep Learning Image Classifier
T2 - IEICE TRANSACTIONS on Information
SP - 709
EP - 722
AU - Erhu LIU
AU - Song HUANG
AU - Cheng ZONG
AU - Changyou ZHENG
AU - Yongming YAO
AU - Jing ZHU
AU - Shiqi TANG
AU - Yanqiu WANG
PY - 2021
DO - 10.1587/transinf.2020EDP7162
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2021
AB - During the recent several years, deep learning has achieved excellent results in image recognition, voice processing, and other research areas, which has set off a new upsurge of research and application. Internal defects and external malicious attacks may threaten the safe and reliable operation of a deep learning system and even cause unbearable consequences. The technology of testing deep learning systems is still in its infancy. Traditional software testing technology is not applicable to test deep learning systems. In addition, the characteristics of deep learning such as complex application scenarios, the high dimensionality of input data, and poor interpretability of operation logic bring new challenges to the testing work. This paper focuses on the problem of test case generation and points out that adversarial examples can be used as test cases. Then the paper proposes MTGAN which is a framework to generate test cases for deep learning image classifiers based on Generative Adversarial Network. Finally, this paper evaluates the effectiveness of MTGAN.
ER -