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Hacking artificial intelligence :a leader's guide from deepfakes to breaking deep learning /
紀錄類型:
書目-語言資料,印刷品 : 單行本
正題名/作者:
Hacking artificial intelligence :/ Davey Gibian.
其他題名:
a leader's guide from deepfakes to breaking deep learning /
作者:
Gibian, Davey,
出版者:
Lanham, Maryland :Rowman & Littlefield, an imprint of The Rowman & Littlefield Publishing Group, Inc.,c2022.
面頁冊數:
ix, 179 p. :ill. ;22 cm.
標題:
Information technology - Management. -
ISBN:
9781538155080
ISBN:
1538155087
ISBN:
9781538155097
Hacking artificial intelligence :a leader's guide from deepfakes to breaking deep learning /
Gibian, Davey,1988-
Hacking artificial intelligence :
a leader's guide from deepfakes to breaking deep learning /Davey Gibian. - Lanham, Maryland :Rowman & Littlefield, an imprint of The Rowman & Littlefield Publishing Group, Inc.,c2022. - ix, 179 p. :ill. ;22 cm.
Includes bibliographical references and index.
Introduction: Hacking facial recognition -- A brief overview of artificial intelligence -- How AI is different from traditional software -- Data bias -- Hacking AI systems -- Evasion attacks -- Data poisoning -- Model inversion ("privacy") attacks -- Obfuscation attacks -- Talking to AI : model interpretability -- Machine vs. machine -- Will someone hack my AI? -- The machine told us to do it.
"The ability to hack AI and the technology industry's lack of effort to secure it is thought by experts to be the biggest unaddressed technology issue of our time. This book sheds light on the many hacking risks, encouraging readers to address problems before they become catastrophic"--
ISBN: 9781538155080NT1042
LCCN: 2021062446Subjects--Topical Terms:
147814
Information technology
--Management.
LC Class. No.: Q335 / .G53 2022
Dewey Class. No.: 006.3
Hacking artificial intelligence :a leader's guide from deepfakes to breaking deep learning /
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a leader's guide from deepfakes to breaking deep learning /
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Introduction: Hacking facial recognition -- A brief overview of artificial intelligence -- How AI is different from traditional software -- Data bias -- Hacking AI systems -- Evasion attacks -- Data poisoning -- Model inversion ("privacy") attacks -- Obfuscation attacks -- Talking to AI : model interpretability -- Machine vs. machine -- Will someone hack my AI? -- The machine told us to do it.
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