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This is the ''' | This is the '''SSFWIKI glossary''' with limited dictionary function. See '''[[resources]]''' for examples you will often find linked for your convenience. | ||
'''Glossaries elsewhere''' | |||
* <section begin=US-Congress-glossary />[https://www.congress.gov/help/legislative-glossary '''Glossary of Legislative Terms''' at congress.gov]<section end=US-Congress-glossary /> | |||
= ACM = | = ACM = | ||
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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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= Audio forensics = | |||
{{Q|'''[[w:Audio forensics]]''' is the field of [[w:forensic science]] relating to the acquisition, analysis, and evaluation of [[w:sound recording]]s that may ultimately be presented as admissible evidence in a court of law or some other official venue.<ref>{{cite web|url=http://www.soundonsound.com/sos/jan10/articles/forensics.htm|title=An Introduction To Forensic Audio|author=Phil Manchester|date=January 2010|publisher=Sound on Sound}}</ref><ref name=maherieee2009>{{cite journal|last=Maher|first=Robert C.|title=Audio forensic examination: authenticity, enhancement, and interpretation|journal=IEEE Signal Processing Magazine|volume=26|issue=2|pages=84–94|date=March 2009|doi=10.1109/msp.2008.931080|s2cid=18216777}}</ref><ref name=gelfandwired2007>{{cite web|url=https://www.wired.com/science/discoveries/news/2007/10/audio_forensics |title=Audio Forensics Experts Reveal (Some) Secrets |date=10 October 2007 |author=Alexander Gelfand |publisher=Wired Magazine |url-status=dead |archiveurl=https://web.archive.org/web/20120408153708/http://www.wired.com/science/discoveries/news/2007/10/audio_forensics |archivedate=2012-04-08 }}</ref><ref name=":0">{{Cite book |last=Maher |first=Robert C. |title=Principles of forensic audio analysis |publisher=Springer |year=2018 |isbn=9783319994536 |location=Cham, Switzerland |pages= |oclc=1062360764}}</ref>|Wikipedia|[[w:Audio forensics]]<ref group="permalink">https://en.wikipedia.org/w/index.php?title=Audio_forensics&oldid=1096442057 loaned on Friday 2022-10-21</ref>}} | |||
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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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= Cyberbullying = | |||
* '''[[w:Cyberbullying]]''' or '''cyberharassment''' is a form of [[w:bullying]] or [[w:harassment]] using [[w:Electronic communication network|w:electronic means]]. Cyberbullying and cyberharassment are also known as '''online bullying'''. ([https://en.wikipedia.org/w/index.php?title=Cyberbullying&oldid=1065285058 Wikipedia]) | |||
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= Generative adversial network = | = Generative adversial network = | ||
[[File:Woman | [[File:Woman 1.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 on [[w: | {{Q|Wikipedia does not have an article on media forensics, but it does have one on [[w:audio forensics]]|juboxi|2022-10-21}} | ||
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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'''. | Since 2021-03-15 a [[w:Relighting]] is a redirect to [[w:Polynomial texture mapping]]. | ||
'''[[w:Polynomial texture mapping]]''' ('''PTM'''), also known as '''Reflectance Transformation Imaging''' (RTI), is a technique of [[w:digital imaging]] and [[w:interactive media|w:interactively]] displaying objects under varying [[w:lighting]] conditions to reveal surface phenomena. ([https://en.wikipedia.org/w/index.php?title=Polynomial_texture_mapping&oldid=1021704138 Wikipedia]) | |||
Past: As of 2020-11-19 Wikipedia does not have an article on '''relighting'''. | |||
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= Sexual bullying = | |||
* '''[[w:Sexual bullying]]''' is a type of [[w:bullying]] and [[w:sexual harassment]] that occurs in connection with a person's [[w:sex]], body, [[w:sexual orientation]] or with [[w:sexual activity]]. It can be [[w:physical abuse|w:physical]], [[w:verbal abuse|w:verbal]], and/or [[w:emotional abuse]]. ([https://en.wikipedia.org/w/index.php?title=Sexual_bullying&oldid=1063305355 Wikipedia]) | |||
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= SISE = | |||
'''SISE''' refers to the [[#Stopping Internet Sexual Exploitation Act]] - a House of Commons of Canada bill introduced in 2022. | |||
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= SISEA = | |||
* [[#Stop Internet Sexual Exploitation Act]] - a US Senate bill in the 2019-2020 session | |||
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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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= Stop Internet Sexual Exploitation Act = | |||
The Stop Internet Sexual Exploitation Act (SISE) was a bill introduced to the 2019-2020 session of the US Senate. | |||
* [https://www.congress.gov/bill/116th-congress/senate-bill/5054?r=1&s=1 US Senate Bill ''''''S.5054''' - '''Stop Internet Sexual Exploitation Act''''' at congress.gov] | |||
= Synthetic | = Stopping Internet Sexual Exploitation Act = | ||
* [https://www.parl.ca/DocumentViewer/en/44-1/bill/C-270/first-reading House of Commons of Canada bill C-270 ''''''Stopping Internet Sexual Exploitation Act'''''' at parl.ca], first read to the Commons on Thursday 2022-04-28. According to [https://www.townandcountrytoday.com/local-news/mp-submits-private-members-bill-for-second-time-5346232 townandcountrytoday.com] the author of the bill introduced an identical bill C-302 on Thursday 2021-05-27, but that got killed off by oncoming federal elections | |||
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= Synthetic pornography = | |||
'''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 '''concensual 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:]]''' for translations, descriptions and links to WMF wikis about transfer learning. | See '''[[wikidata:Q6027324]]''' 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:]]''' for translations, descriptions and links to WMF wikis about voice changers. | See '''[[wikidata:Q4224062]]''' for translations, descriptions and links to WMF wikis about voice changers. | ||
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= Permalinks = | |||
<references group="permalink"/> | |||
= References = | = References = | ||
<references /> | <references /> | ||
[[Category:Listing]] | |||
[[Category:In English]] | |||
[[Category:In Finnish]] | |||
[[Category:Suomeksi]] | |||