| 한글제목(Korean Title) |
ActiveBoostThief: 신뢰할 수 있는 능동적 학습을 이용한 모델 추출 공격 |
| 영문제목(English Title) |
ActiveBoostThief: Model Extraction Attack Using Reliable Active Learning |
| 저자(Author) |
남영은
강준혁
이재길
Youngeun Nam
Junhyeok Kang
Jae-Gil Lee
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| 원문수록처(Citation) |
VOL 48 NO. 01 PP. 0594 ~ 0596 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
As machine learning models are being applied in practice, the security for models is becoming more significant. A model extraction attack, one of the types of adversarial attack, exploits the open application programming interfaces (APIs) to figure out the object model. Prior works for model extraction using active learning have a lack of confidence in the thief model when selecting the instances for queries. We propose ActiveBoostThief framework that accomplishes a model extraction using active learning to complement the model’s reliability. We demonstrate with experiments that our model improves the reliability of the substitute model and enables more accurate performance in the model extraction attack compared with the existing state-of-the-art baseline, ActiveThief. |
| 키워드(Keyword) |
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| 파일첨부 |
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