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Please sign and share the petition 'Tighten regulation on taking, making and faking explicit images' at Change.org initiated by Helen Mort to the w:Law Commission (England and Wales) to properly update UK laws against synthetic filth. Only name and email required to support, no nationality requirement. See Current and possible laws and their application @ #SSF! wiki for more info on the struggle for laws to protect humans.

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Glossary: Difference between revisions

1,221 bytes added ,  5 years ago
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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