December 2020
Beginner to intermediate
256 pages
5h 15m
Korean
Content preview from 안전한 인공지능 시스템을 위한 심층 신경망 강화
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236
4
부
방어
예를 들어, 이미지의 일부 픽셀은 비적대적 데이터에 대한 이미지 분류를 결정하는 데 중요하
지 않지만
DNN
이 잘못된 결과를 생성하도록 적대적 사례에 이용할 수 있습니다. 데이터에서
이러한 픽셀을 제거하면 ‘좋은’ 데이터의 예측에는 부정적 영향을 미치지 않으면서 ‘적대성’을
제거할 수 있습니다. 이와 유사하게, 음성 임계값을 벗어난 오디오 주파수를 제거하면 음성 인
식 시스템의
효과에 영향을 미치지 않으면서 음성 범위 밖에서 적대적 입력을 생성할 가능성이 제거됩니다.
강구해온 접근법 하나는 의사결정에 가장 큰 영향을 미치는 데이터 특성을 식별하는 수학 기법
PCA
Principal
Component
Analysis
를 사용하는 것입니다.
PCA
는 좋은 데이터에 대한 결정에 영향을 미
치지 않는 적대적 사례에 의해 어떤 데이터 부분이 악용되는지 확인하는 방법으로 테스트되었
습니다. 애석하게도, ‘적대성’에 영향을 미치는 뚜렷한 특성이 없는 것으로 판명되었으므로 아
직 효과적인 방어 수단은 아닙니다.
17
10.3
대상 감추기
대상 은닉에서 중요한 점 하나는
DNN
만을 대상으로 하지 않는다는 사실입니다. 즉 방어 메커
니즘을 포함한 완벽한 처리 시스템도 대상으로 합니다.
7
장에서는 실제 세계에서 견고한 적대적 콘텐츠를 생성해야 하는 과제를 살펴보았습니다. ...
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