December 2020
Beginner to intermediate
256 pages
5h 15m
Korean
Content preview from 안전한 인공지능 시스템을 위한 심층 신경망 강화
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173
8
장
물리적 공격
7
장에서는 디지털 이미지의 섭동이나 오디오의 왜곡으로 적대적 입력을 어떻게 생성하는지를
중점적으로 알아보았습니다. 그러나 공격자가 디지털 데이터 형식에 접근하지 못하는 경우가
많습니다. 즉 공격자는 데이터가 생성되는 물리적 세계에만 영향을 미칠 수 있습니다. 그 구분
은 대상 처리 시스템이 입력을 외부에서 디지털 콘텐츠(예: 소셜 미디어에 업로드되는 데이터 )
의 형태로 가져오는지 아니면 센서에서 직접 가져오는지에 좌우됩니다. 앞으로 살펴볼 물리적
세계의 공격에서 발생하는 위협은 앞 장에서 설명한 디지털 세계와는 상당히 다릅니다.
물리적 세계에서 적대적 사례를 생성하는 일은 공격자에게 새로운 도전입니다. 이제 공격자는
실제로 존재하는 무언가를 만들어내거나 변경해서 적대적 섭동이나 패치를 물리적으로 표현해
야 합니다. 카메라를 통해 수신되는 적대적 데이터의 경우, 변경되는 것은
2D
인쇄물 또는
3D
객체일 수 있습니다. 이와 마찬가지로, 마이크는 컴퓨터나 텔레비전과 같은 디지털 장치를 통
해 환경에서 재생되는 조작된 오디오 샘플로부터 적대적 왜곡을 받을 수 있습니다. 예를 들어,
공격자는 적대적 물체가 조명 상태나 카메라 위치에 상관없이 견고하게 상태를 유지하도록 어
떻게 보장할 수 있을까요? 또는 생성된 적대적 콘텐츠에 대한 어시스턴트의 ...
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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.
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