December 2020
Beginner to intermediate
256 pages
5h 15m
Korean
Content preview from 안전한 인공지능 시스템을 위한 심층 신경망 강화
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191
9
장
모델 견고성 평가
9
장에서는 방어 탐색을 시작하기 위해 적대적 사례에 대한
DNN
모델의 견고성을 평가하는 방
법을 살펴봅니다. 이 내용은
10
장에서 설명하는 방어의 효과를 이해하는 데 기초가 됩니다.
DNN
구성요소의 견고성을 개별적으로 평가하면 모델과 방어 접근법을 객관적으로 비교할 수
있습니다. 예를 들어, 이 평가는 새로운 방어 접근법이 이전 방법보다 더 효과적인지 덜 효과
적인지를 알아보려는 연구 목적으로 수행할 수 있습니다. 또 다른 목적으로는 조직에서 최근에
배포한 모델이 이전 버전 이상으로 안전함을 확실히 하기 위해 평가를 수행할 수 있습니다.
모델 평가에는 비교에 사용할 지표의 객관성을 보장할 일관된 방법론과 측정법이 필요합니다.
하지만 안타깝게도 적대적 사례에 대항하는 신경망의 방어력을 지표로 나타내는 것은 간단하
지 않습니다. 이 점을 해결하기 위해서 다음 질문의 답을 찾아보겠습니다. ‘무엇을’ 방어할 것인
가?
7
장과
8
장에서 배운 내용을 바탕으로 방어를 위협하는 모델링 방법부터 살펴보는데, 이것
이 앞으로 논의할 '위협 모델'입니다 (
9
.
1
참조).
CAUTION
_
완전한 정보 평가
방어를 평가할 때 대상 시스템의 내부 작동을 기밀로 삼는 것 자체를 방어로 간주해서는 안 됨을 유념하기 바
랍니다. 정보 보안 관행은 ‘불명확한
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.