Synthetic human-like fakes

From Stop Synthetic Filth! wiki
Revision as of 17:22, 27 June 2020 by Juho Kunsola (talk | contribs) (→‎Media perhaps about synthetic human-like fakes: + === 3rd century BC === + The '''w:Book of Daniel''' was put in writing + See Biblical explanation - The books of Daniel and Revelations § Daniel 7 and cautioning about explicit written content + fixed link)
Jump to navigation Jump to search

When the camera does not exist, but the subject being imaged with a simulation of a (movie) camera deceives the watcher to believe it is some living or dead person it is a digital look-alike.

When it cannot be determined by human testing whether some fake voice is a synthetic fake of some person's voice, or is it an actual recording made of that person's actual real voice, it is a digital sound-alike.


Image 2 (low resolution rip)

(1) Sculpting a morphable model to one single picture

(2) Produces 3D approximation

(4) Texture capture

(3) The 3D model is rendered back to the image with weight gain

(5) With weight loss

(6) Looking annoyed

(7) Forced to smile Image 2 by Blanz and Vettel – Copyright ACM 1999 – http://dl.acm.org/citation.cfm?doid=311535.311556 – Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

Digital look-alikes

A Computer Animated Hand is a 1972 short film by Edwin Catmull and Fred Parke. This was the first time that computer-generated imagery was used in film to animate likenesses of moving human appearance.

Introduction to digital look-alikes

Subtraction of the diffuse reflection from the specular reflection yields the specular component of the model's reflectance.

Original picture by Debevec et al. - Copyright ACM 2000 https://dl.acm.org/citation.cfm?doid=311779.344855

In the cinemas we have seen digital look-alikes for over 15 years. These digital look-alikes have "clothing" (a simulation of clothing is not clothing) or "superhero costumes" and "superbaddie costumes", and they don't need to care about the laws of physics, let alone laws of physiology. It is generally accepted that digital look-alikes made their public debut in the sequels of The Matrix i.e. w:The Matrix Reloaded and w:The Matrix Revolutions released in 2003. It can be considered almost certain, that it was not possible to make these before the year 1999, as the final piece of the puzzle to make a (still) digital look-alike that passes human testing, the reflectance capture over the human face, was made for the first time in 1999 at the w:University of Southern California and was presented to the crème de la crème of the computer graphics field in their annual gathering SIGGRAPH 2000.[1]


“Do you think that was Hugo Weaving's left cheekbone that Keanu Reeves punched in with his right fist?”

~ Trad on The Matrix Revolutions


The problems with digital look-alikes

Image 1: Separating specular and diffuse reflected light

(a) Normal image in dot lighting

(b) Image of the diffuse reflection which is caught by placing a vertical polarizer in front of the light source and a horizontal in the front the camera

(c) Image of the highlight specular reflection which is caught by placing both polarizers vertically

(d) Subtraction of c from b, which yields the specular component

Images are scaled to seem to be the same luminosity.

Original image by Debevec et al. – Copyright ACM 2000 – https://dl.acm.org/citation.cfm?doid=311779.344855 – Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

Extremely unfortunately for the humankind, organized criminal leagues, that posses the weapons capability of making believable looking synthetic pornography, are producing on industrial production pipelines synthetic terror porn[footnote 1] by animating digital look-alikes and distributing it in the murky Internet in exchange for money stacks that are getting thinner and thinner as time goes by.

These industrially produced pornographic delusions are causing great humane suffering, especially in their direct victims, but they are also tearing our communities and societies apart, sowing blind rage, perceptions of deepening chaos, feelings of powerlessness and provoke violence. This hate illustration increases and strengthens hate thinking, hate speech, hate crimes and tears our fragile social constructions apart and with time perverts humankind's view of humankind into an almost unrecognizable shape, unless we interfere with resolve.

For these reasons the bannable raw materials i.e. covert models, needed to produce this disinformation terror on the information-industrial production pipelines, should be prohibited by law in order to protect humans from arbitrary abuse by criminal parties.

List of possible naked digital look-alike attacks

  • The classic "portrayal of as if in involuntary sex"-attack. (Digital look-alike "cries")
  • "Sexual preference alteration"-attack. (Digital look-alike "smiles")

How to counter synthetic porn: Adequate Porn Watcher AI (transcluded)

Adequate Porn Watcher AI (APW_AI) is an w:AI and w:computer vision concept to search for any and all porn that should not be by watching and modeling all porn ever found on the w:Internet thus effectively protecting humans by exposing covert naked digital look-alike attacks and also other contraband.

Obs. #A service identical to APW_AI used to exist - FacePinPoint.com

The method and the effect

The method by which APW_AI would be providing safety and security to its users, is that they can briefly upload a model they've gotten of themselves and then the APW_AI will either say nothing matching found or it will be of the opinion that something matching found.

If people are able to check whether there is synthetic porn that looks like themselves, this causes synthetic hate-illustration industrialists' product lose destructive potential and the attacks that happen are less destructive as they are exposed by the APW_AI and thus decimate the monetary value of these disinformation weapons to the criminals.

If you feel comfortable to leave your model with the good people at the benefactor for safekeeping you get alerted and help if you ever get attacked with a synthetic porn attack.

Rules

Looking up if matches are found for anyone else's model is forbidden and this should probably be enforced with a facial w:biometric w:facial recognition system app that checks that the model you want checked is yours and that you are awake.

Definition of adequacy

An adequate implementation should be nearly free of false positives, very good at finding true positives and able to process more porn than is ever uploaded.

What about the people in the porn-industry?

People who openly do porn can help by opting-in to help in the development by providing training material and material to test the AI on. People and companies who help in training the AI naturally get credited for their help.

There are of course lots of people-questions to this and those questions need to be identified by professionals of psychology and social sciences.

History

The idea of APW_AI occurred to User:Juho Kunsola on Friday 2019-07-12. Subsequently (the next day) this discovery caused the scrapping of the plea to ban convert modeling of human appearance as that would have rendered APW_AI legally impossible.


Resources

Tools

Legal

Traditional porn-blocking

Traditional porn-blocking done by w:some countries seems to use w:DNS to deny access to porn sites by checking if the domain name matches an item in a porn sites database and if it is there then it returns an unroutable address, usually w:0.0.0.0.

Topics on github.com

Curated lists and databases

Porn blocking services

Software for nudity detection

Links regarding pornography censorship

Against pornography

Technical means of censorship and how to circumvent

Countermeasures elsewhere

Partial transclusions from Organizations, studies and events against synthetic human-like fakes below


Organizations against synthetic human-like fakes

AI incident repositories

Help for victims of image or audio based abuse

Awareness and countermeasures

w:California w:Senator w:Connie Leyva sponsored California Senate Bill SB 564 - Depiction of individual using digital or electronic technology: sexually explicit material: cause of action in Feb 2019. It is identical to Assembly Bill 602 authored by w:Marc Berman. The bill was endorsed by SAG-AFTRA. It became law on 1 January 2020 in the w:California Civil Code of the w:California Codes.

Organizations for media forensics

The Defense Advanced Research Projects Agency, better known as w:DARPA has been active in the field of countering synthetic fake video for longer than the public has been aware of the problems existing.

A service identical to APW_AI used to exist - FacePinPoint.com

Transcluded from FacePinPoint.com


FacePinPoint.com was a for-a-fee service from 2017 to 2021 for pointing out where in pornography sites a particular face appears, or in the case of synthetic pornography, a digital look-alike makes make-believe of a face or body appearing.[contacted 2]The inventor and founder of FacePinPoint.com, Mr. Lionel Hagege registered the domain name in 2015[8], when he set out to research the feasibility of his action plan idea against non-consensual pornography.[9] The description of how FacePinPoint.com worked is the same as Adequate Porn Watcher AI (concept)'s description.


Organizations possibly against synthetic human-like fakes

Originally harvested from the study The ethics of artificial intelligence: Issues and initiatives (.pdf) by the w:European Parliamentary Research Service, published on the w:Europa (web portal) in March 2020.[1st seen in 5]

Services that should get back to the task at hand - FacePinPoint.com

Transcluded from FacePinPoint.com

FacePinPoint.com was a for-a-fee service from 2017 to 2021 for pointing out where in pornography sites a particular face appears, or in the case of synthetic pornography, a digital look-alike makes make-believe of a face or body appearing.[contacted 8]The inventor and founder of FacePinPoint.com, Mr. Lionel Hagege registered the domain name in 2015[10], when he set out to research the feasibility of his action plan idea against non-consensual pornography.[11] The description of how FacePinPoint.com worked is the same as Adequate Porn Watcher AI (concept)'s description.

Other essential developments

Studies against synthetic human-like fakes

Detecting deep-fake audio through vocal tract reconstruction

Detecting deep-fake audio through vocal tract reconstruction is an epic scientific work, against fake human-like voices, from the w:University of Florida in published to peers in August 2022.

University of Florida, the home of Florida Institute for Cybersecurity Research (FICS) at fics.institute.ufl.edu, that brought us the Detecting deep-fake audio through vocal tract reconstruction system to hunt down the fake human-like voices.
The Office of Naval Research (ONR) at nre.navy.mil of the USA funded this breakthrough science.

The work Who Are You (I Really Wanna Know)? Detecting Audio DeepFakes Through Vocal Tract Reconstruction at usenix.org, presentation page, version included in the proceedings[12] and slides from researchers of the Florida Institute for Cybersecurity Research (FICS) at fics.institute.ufl.edu in the w:University of Florida received funding from the w:Office of Naval Research and was presented on 2022-08-11 at the 31st w:USENIX Security Symposium.

This work was done by PhD student Logan Blue, Kevin Warren, Hadi Abdullah, Cassidy Gibson, Luis Vargas, Jessica O’Dell, Kevin Butler and Professor Patrick Traynor.

The University of Florida Research Foundation Inc has filed for and received an US patent titled 'Detecting deep-fake audio through vocal tract reconstruction' registration number US20220036904A1 (link to patents.google.com) with 20 claims. The patent application was published on Thursday 2022-02-03. The patent application was approved on 2023-07-04 and has an adjusted expiration date of 2041-12-29.

Protecting world leaders against deep fakes using facial, gestural, and vocal mannerisms

Protecting President Zelenskyy against deep fakes

Other studies against synthetic human-like fakes

Legal information compilations


More studies can be found in the SSFWIKI Timeline of synthetic human-like fakes

Search for more

Reporting against synthetic human-like fakes

Companies against synthetic human-like fakes See resources for more.

Events against synthetic human-like fakes

Upcoming events

In reverse chronological order

Ongoing events

Past events


  • 2019 | At the annual Finnish w:Ministry of Defence's Scientific Advisory Board for Defence (MATINE) public research seminar, a research group presented their work 'Synteettisen median tunnistus'' at defmin.fi (Recognizing synthetic media). They developed on earlier work on how to automatically detect synthetic human-like fakes and their work was funded with a grant from MATINE.
  • 2018 | NIST NIST 'Media Forensics Challenge 2018' at nist.gov was the second annual evaluation to support research and help advance the state of the art for image and video forensics technologies – technologies that determine the region and type of manipulations in imagery (image/video data) and the phylogenic process that modified the imagery.
  • 2016 | Nimble Challenge 2016 - NIST released the Nimble Challenge’16 (NC2016) dataset as the MFC program kickoff dataset, (where NC is the former name of MFC). [24]


Sources for technologies

Synthethic-Media-Landscape.jpg
A map of technologies courtesy of Samsung Next, linked from 'Why it’s time to change the conversation around synthetic media' at venturebeat.com[1st seen in 7]

See also

Image 1: Separating specular and diffuse reflected light

(a) Normal image in dot lighting

(b) Image of the diffuse reflection which is caught by placing a vertical polarizer in front of the light source and a horizontal in the front the camera

(c) Image of the highlight specular reflection which is caught by placing both polarizers vertically

(d) The difference of c and b yields the specular highlight component

Images are scaled to seem to be the same luminosity.

Original image by Debevec et al. – Copyright ACM 2000 – https://dl.acm.org/citation.cfm?doid=311779.344855 – Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

Biblical connection - Revelation 13 and Daniel 7, wherein Daniel 7 and Revelation 13 we are warned of this age of industrial filth.

In Revelation 19:20 it says that the beast is taken prisoner, can we achieve this without 'APW_AI?

'Saint John on Patmos' pictures w:John of Patmos on w:Patmos writing down the visions to make the w:Book of Revelation

'Saint John on Patmos' from folio 17 of the w:Très Riches Heures du Duc de Berry (1412-1416) by the w:Limbourg brothers. Currently located at the w:Musée Condé 40km north of Paris, France.

References

  1. ↑ Debevec, Paul (2000). "Acquiring the reflectance field of a human face". Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00. ACM. pp. 145–156. doi:10.1145/344779.344855. ISBN 978-1581132083. Retrieved 2017-05-24.
  2. ↑ "Microsoft tip led police to arrest man over child abuse images". w:The Guardian. 2014-08-07.
  3. ↑ https://www.partnershiponai.org/aiincidentdatabase/
  4. ↑ whois aiaaic.org
  5. ↑ https://charliepownall.com/ai-algorithimic-incident-controversy-database/
  6. ↑ https://web.archive.org/web/20160630154819/https://www.darpa.mil/program/media-forensics
  7. ↑ https://web.archive.org/web/20191108090036/https://www.darpa.mil/program/semantic-forensics November
  8. ↑ whois facepinpoint.com
  9. ↑ https://www.facepinpoint.com/aboutus
  10. ↑ whois facepinpoint.com
  11. ↑ https://www.facepinpoint.com/aboutus
  12. ↑ Blue, Logan; Warren, Kevin; Abdullah, Hadi; Gibson, Cassidy; Vargas, Luis; O’Dell, Jessica; Butler, Kevin; Traynor, Patrick (August 2022). "Detecting deep-fake audio through vocal tract reconstruction". Proceedings of the 31st USENIX Security Symposium: 2691–2708. ISBN 978-1-939133-31-1. Retrieved 2022-10-06.
  13. ↑ Boháček, Matyáš; Farid, Hany (2022-11-23). "Protecting world leaders against deep fakes using facial, gestural, and vocal mannerisms". w:Proceedings of the National Academy of Sciences of the United States of America. 119 (48). doi:10.1073/pnas.221603511. Retrieved 2023-01-05.
  14. ↑ Boháček, Matyáš; Farid, Hany (2022-06-14). "Protecting President Zelenskyy against Deep Fakes". arXiv:2206.12043 [cs.CV].
  15. ↑ Lawson, Amanda (2023-04-24). "A Look at Global Deepfake Regulation Approaches". responsible.ai. Responsible Artificial Intelligence Institute. Retrieved 2024-02-14.
  16. ↑ Williams, Kaylee (2023-05-15). "Exploring Legal Approaches to Regulating Nonconsensual Deepfake Pornography". techpolicy.press. Retrieved 2024-02-14.
  17. ↑ Owen, Aled (2024-02-02). "Deepfake laws: is AI outpacing legislation?". onfido.com. Onfido. Retrieved 2024-02-14.
  18. ↑ Pirius, Rebecca (2024-02-07). "Is Deepfake Pornography Illegal?". Criminaldefenselawyer.com. w:Nolo (publisher). Retrieved 2024-02-22.
  19. ↑ Rastogi, Janvhi (2023-10-16). "Deepfake Pornography: A Legal and Ethical Menace". tclf.in. The Contemporary Law Forum. Retrieved 2024-02-14.
  20. ↑ https://www.nist.gov/itl/iad/mig/open-media-forensics-challenge
  21. ↑ https://law.yale.edu/isp/events/technologies-deception
  22. ↑ https://venturebeat.com/2020/06/12/facebook-detection-challenge-winners-spot-deepfakes-with-82-accuracy/
  23. ↑ https://www.nist.gov/itl/iad/mig/open-media-forensics-challenge

1st seen in

  1. ↑ 1.0 1.1 1.2 1.3 Seen first in https://github.com/topics/porn-block, meta for actual use. The topic was stumbled upon.
  2. ↑ 2.0 2.1 Seen first in https://github.com/topics/pornblocker Saw this originally when looking at https://github.com/topics/porn-block Topic
  3. ↑ 3.0 3.1 3.2 Seen first in https://github.com/topics/porn-filter Saw this originally when looking at https://github.com/topics/porn-block Topic
  4. ↑ https://www.iwf.org.uk/our-technology/report-remove/
  5. ↑ 5.00 5.01 5.02 5.03 5.04 5.05 5.06 5.07 5.08 5.09 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 "The ethics of artificial intelligence: Issues and initiatives" (PDF). w:Europa (web portal). w:European Parliamentary Research Service. March 2020. Retrieved 2021-02-17. This study deals with the ethical implications and moral questions that arise from the development and implementation of artificial intelligence (AI) technologies.
  6. ↑ https://www.reuters.com/article/us-cyber-deepfake-activist/deepfake-used-to-attack-activist-couple-shows-new-disinformation-frontier-idUSKCN24G15E
  7. ↑ venturebeat.com found via some Facebook AI & ML group or page yesterday. Sorry, don't know precisely right now.


Digital sound-alikes

Living people can defend[footnote 2] themselves against digital sound-alike by denying the things the digital sound-alike says if they are presented to the target, but dead people cannot. Digital sound-alikes offer criminals new disinformation attack vectors and wreak havoc on provability.

Timeline of digital sound-alikes

  • As of 2019 Symantec research knows of 3 cases where digital sound-alike technology has been used for crimes.[1]

'Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis' 2018 by Google Research (external transclusion)

The Iframe below is transcluded from 'Audio samples from "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis"' at google.gituhub.io, the audio samples of a sound-like-anyone machine presented as at the 2018 w:NeurIPS conference by Google researchers.

Documented digital sound-alike attacks

Possible legal response: Outlawing digital sound-alikes (transcluded)

Transcluded from Juho's proposal on banning digital sound-alikes

Example of a hypothetical 4-victim digital sound-alike attack

A very simple example of a digital sound-alike attack is as follows:

Someone puts a digital sound-alike to call somebody's voicemail from an unknown number and to speak for example illegal threats. In this example there are at least two victims:

  1. Victim #1 - The person whose voice has been stolen into a covert model and a digital sound-alike made from it to frame them for crimes
  2. Victim #2 - The person to whom the illegal threat is presented in a recorded form by a digital sound-alike that deceptively sounds like victim #1
  3. Victim #3 - It could also be viewed that victim #3 is our law enforcement systems as they are put to chase after and interrogate the innocent victim #1
  4. Victim #4 - Our judiciary which prosecutes and possibly convicts the innocent victim #1.

Thus it is high time to act and to criminalize the covert modeling of human appearance and voice!

Examples of speech synthesis software not quite able to fool a human yet

Some other contenders to create digital sound-alikes are though, as of 2019, their speech synthesis in most use scenarios does not yet fool a human because the results contain tell tale signs that give it away as a speech synthesizer.

The below video 'This AI Clones Your Voice After Listening for 5 Seconds' by '2 minute papers' describes the voice thieving machine presented by Google Research in NeurIPS 2018.

Video 'This AI Clones Your Voice After Listening for 5 Seconds' by '2 minute papers' describes the voice thieving machine by Google Research in NeurIPS 2018.
A spectrogram of a male voice saying 'nineteenth century'


Media perhaps about synthetic human-like fakes

This is a chronological listing of media that are probably to do with synthetic human-like fakes.


6th century BC

Image taken from Silos Apocalypse. Originally published/produced in Spain (Silos), 1109.

Daniel 7, Daniel's vision of the three beasts Dan 7:1-6 and the fourth beast Dan 7:7-8 from the sea and the Ancient of DaysDan 7:9-10


3rd century BC

Footnotes

  1. ↑ It is terminologically more precise, more inclusive and more useful to talk about 'synthetic terror porn', if we want to talk about things with their real names, than 'synthetic rape porn', because also synthesizing recordings of consentual looking sex scenes can be terroristic in intent.
  2. ↑ Whether a suspect can defend against faked synthetic speech that sounds like him/her depends on how up-to-date the judiciary is. If no information and instructions about digital sound-alikes have been given to the judiciary, they likely will not believe the defense of denying that the recording is of the suspect's voice.

References

  1. ↑ https://www.washingtonpost.com/technology/2019/09/04/an-artificial-intelligence-first-voice-mimicking-software-reportedly-used-major-theft/


Cite error: <ref> tags exist for a group named "contact", but no corresponding <references group="contact"/> tag was found
Cite error: <ref> tags exist for a group named "contacted", but no corresponding <references group="contacted"/> tag was found

Retrieved from "https://stop-synthetic-filth.org/w/index.php?title=Synthetic_human-like_fakes&oldid=1363"