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sourced definition of = Generative adversial network = from Wikipedia into a {{Q}}. GANs are frighteningly good at faking 2D pictures of (non-)existing people.
(+ = Media forensics = + Media forensics deal with ascertaining genuinity of media.)
(sourced definition of = Generative adversial network = from Wikipedia into a {{Q}}. GANs are frighteningly good at faking 2D pictures of (non-)existing people.)
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= Digital sound-alike =
= Digital sound-alike =
When it cannot be determined by human testing, is some synthesized recording a simulation of some person's speech, or is it a recording made of that person's actual real voice, it is a '''[[digital sound-alikes|digital sound-alike]]'''.  
When it cannot be determined by human testing, is some synthesized recording a simulation of some person's speech, or is it a recording made of that person's actual real voice, it is a '''[[digital sound-alikes|digital sound-alike]]'''.  
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= Generative adversial network =
{{Q|A '''generative adversarial network''' ('''GAN''') is a class of [[w:machine learnin|g]] systems. Two [[w:neural network|neural network]]s contest with each other in a [[w:zero-sum game|zero-sum game]] framework. This technique can generate photographs that look at least superficially authentic to human observers,<ref name="GANnips" /><ref name="GANs">{{cite arXiv |eprint=1406.2661|title=Generative Adversarial Networks|first1=Ian |last1=Goodfellow |first2=Jean |last2=Pouget-Abadie |first3=Mehdi |last3=Mirza |first4=Bing |last4=Xu |first5=David |last5=Warde-Farley |first6=Sherjil |last6=Ozair |first7=Aaron |last7=Courville |first8=Yoshua |last8=Bengio |class=cs.LG |year=2014 }}</ref> having many realistic characteristics. It is a form of [[w:unsupervised learning|unsupervised learning]]]].<ref name="ITT_GANs">{{cite arXiv |eprint=1606.03498|title=Improved Techniques for Training GANs|last1=Salimans |first1=Tim |last2=Goodfellow |first2=Ian |last3=Zaremba |first3=Wojciech |last4=Cheung |first4=Vicki |last5=Radford |first5=Alec |last6=Chen |first6=Xi |class=cs.LG |year=2016 }}</ref>|Wikipedia|[[w:generative adversarial network|generative adversarial network]]}}


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