DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Choi, Jaesik | - |
dc.contributor.advisor | 최재식 | - |
dc.contributor.author | Park, Seongjin | - |
dc.date.accessioned | 2023-06-22T19:31:28Z | - |
dc.date.available | 2023-06-22T19:31:28Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1008203&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308227 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2022.8,[iii, 24 p. :] | - |
dc.description.abstract | In general, Deep Neural Networks (DNNs) are evaluated by the generalization performance measured on unseen data excluded from the training phase. Along with the development of DNNs, the generalization performance converges to the state-of-the-art and it becomes difficult to evaluate DNNs solely based on this metric. The robustness against adversarial attack has been used as an additional metric to evaluate DNNs by measuring their vulnerability. However, few studies have been conducted to analyze the adversarial robustness in terms of geometry of DNNs, and in particular, there are not enough indicators to represent clear correlations between the internal properties and adversarial robustness. In this work, we perform an empirical study to analyze the internal properties of DNNs that affect model robustness under adversarial attacks. In particular, we propose the novel concept of the Populated Region Set (PRS), where training samples are populated more frequently, to represent the internal properties of DNNs in a practical setting. From systematic experiments with the proposed concept, we provide empirical evidence to validate that a low PRS ratio has a strong relationship with the adversarial robustness of DNNs. We also devise PRS regularizer leveraging the characteristics of PRS to improve the adversarial robustness without adversarial training. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Decision Region▼aAdversarial Robustness▼aRobust Training | - |
dc.subject | 결정 영역▼a적대적 강건성▼a강건 학습 | - |
dc.title | Empirical study of the relationship between decision region and robustness in deep neural networks | - |
dc.title.alternative | 심층 신경망의 결정 영역과 적대적 강건성과의 관계에 대한 경험적 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :김재철AI대학원, | - |
dc.contributor.alternativeauthor | 박성진 | - |
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