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This is the ''' | This is the '''SSF! wiki glossary''' with limited dictionary function. See '''[[resources]]''' for examples you will often find linked for your convenience. | ||
'''Glossaries elsewhere''' | '''Glossaries elsewhere''' | ||
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= Appearance and voice theft = | = Appearance and voice theft = | ||
Appearance is thieved with [[digital look-alikes]] and voice is thieved with [[digital sound-alikes]]. These are new and very extreme forms of identity theft. Ban covert modeling and possession and doing anything with a model of a human's voice, but don't ban the [[Adequate Porn Watcher AI (concept)]]. | Appearance is thieved with [[digital look-alikes]] and voice is thieved with [[digital sound-alikes]]. These are new and very extreme forms of identity theft. Ban covert modeling and possession and doing anything with a model of a human's voice, but don't ban the [[Adequate Porn Watcher AI (concept)]]. | ||
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* Covertly modeling the '''human voice''' | * Covertly modeling the '''human voice''' | ||
There is work ongoing to model e.g. '''human's style of writing''', but this is probably not as drastic a threat as the covert modeling of appearance and of voice. | There is work ongoing to model e.g. '''human's style of writing''', but this is probably not as drastic a threat as the covert modeling of appearance and of voice. | ||
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= Generative adversial network = | = Generative adversial network = | ||
[[File:Woman | [[File:Woman 7.jpg|alt=An image generated by StyleGAN, a generative adversarial network (GAN), that looks deceptively like a portrait of a young woman.|thumb|250x250px|An image generated by [[w:StyleGAN]], a [[w:generative adversarial network]] (GAN), that looks deceptively like a portrait of a young woman.]] | ||
{{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="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 networks]]}} | {{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="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 networks]]}} | ||
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'''Media forensics''' deal with ascertaining genuinity of media. | '''Media forensics''' deal with ascertaining genuinity of media. | ||
{{Q|Wikipedia does not have an article | {{Q|Wikipedia does not have an article on [[w:Media forensics]]|juboxi|2019-04-05}} | ||
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'''Relighting''' means applying a completely different [[w:lighting]] situation to an image or video which has already been imaged. As of 2020-09 the English Wikipedia does not have an article on relighting. | '''Relighting''' means applying a completely different [[w:lighting]] situation to an image or video which has already been imaged. As of 2020-09 the English Wikipedia does not have an article on relighting. | ||
As of 2020-11-19 Wikipedia does not have an article on '''relighting'''. | |||
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= Spectrogram = | = Spectrogram = | ||
[[File:Spectrogram-19thC.png|thumb|right|360px|A [[w:spectrogram|spectrogram]] of a male voice saying 'nineteenth century']] | [[File:Spectrogram-19thC.png|thumb|right|360px|A [[w:spectrogram|spectrogram]] of a male voice saying 'nineteenth century']] | ||
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'''[[w:Spectrogram]]s''' are used extensively in the fields of [[w:music]], [[w:linguistics]], [[w:sonar]], [[w:radar]], [[w:speech processing]], [[w:seismology]], and others. Spectrograms of audio can be used to identify spoken words [[w:phonetics|phonetic]]ally, and to analyse the [[w:Animal communication|various calls of animals]]. (Wikipedia) | '''[[w:Spectrogram]]s''' are used extensively in the fields of [[w:music]], [[w:linguistics]], [[w:sonar]], [[w:radar]], [[w:speech processing]], [[w:seismology]], and others. Spectrograms of audio can be used to identify spoken words [[w:phonetics|phonetic]]ally, and to analyse the [[w:Animal communication|various calls of animals]]. (Wikipedia) | ||
See '''[[wikidata: | See '''[[wikidata:]]''' for translations, descriptions and links to WMF wikis about spectrograms. | ||
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See '''[[wikidata:]]''' for translations, descriptions and links to WMF wikis about speech syntheses. | See '''[[wikidata:]]''' for translations, descriptions and links to WMF wikis about speech syntheses. | ||
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= Synthetic | = Synthetic porn = | ||
'''Synthetic pornography''' is a '''strong technological hallucinogen'''. | '''Synthetic pornography''' is a '''strong technological hallucinogen'''. | ||
== Synthetic terror porn == | == Synthetic terror porn == | ||
'''Synthetic terror porn''' is pornography synthesized with terrorist intent. '''Synthetic rape porn''' is probably by far the most prevalent form of this, but it must be noted that synthesizing ''' | '''Synthetic terror porn''' is pornography synthesized with terrorist intent. '''Synthetic rape porn''' is probably by far the most prevalent form of this, but it must be noted that synthesizing '''concentual looking sex scenes''' can also be '''terroristic''' in intent and effect. | ||
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{{Q|'''Transfer learning (TL)''' is a research problem in [[w:machine learning|machine learning]] (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.|Wikipedia|[[w:Transfer learning|Transfer learning]]}} | {{Q|'''Transfer learning (TL)''' is a research problem in [[w:machine learning|machine learning]] (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.|Wikipedia|[[w:Transfer learning|Transfer learning]]}} | ||
See '''[[wikidata: | See '''[[wikidata:]]''' for translations, descriptions and links to WMF wikis about transfer learning. | ||
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Please see '''[[Resources#List of voice changers]]''' for some alternatives. | Please see '''[[Resources#List of voice changers]]''' for some alternatives. | ||
See '''[[wikidata: | See '''[[wikidata:]]''' for translations, descriptions and links to WMF wikis about voice changers. | ||
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= References = | = References = | ||