Child Sexual Abuse Material (CSAM)
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A Child Sexual Abuse Material (CSAM) is a Pornography that exploits children.
- AKA: Child Pornography.
- See: European Commission, Pornography, Child, Sexual Stimulation, Sexual Assault, Child Sexual Abuse, Simulated Child Pornography, Sexual Acts, Erotic Literature, Photograph, Sculpture, Drawing.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Child_pornography Retrieved:2020-10-6.
- Child pornography (also called child porn or sometimes abbreviated as CP) is pornography that exploits children for sexual stimulation. It may be produced with the direct involvement or sexual assault of a child (also known as child sexual abuse images ) or it may be simulated child pornography. Abuse of the child occurs during the sexual acts or lascivious exhibitions of genitals or pubic areas which are recorded in the production of child pornography. Child pornography may use a variety of mediums, including writings, magazines, photos, sculpture, drawing, cartoon, painting, animation, sound recording, film, video, and video games. Child pornography may be created for profit or other reasons. Laws regarding child pornography generally include sexual images involving prepubescents, pubescent, or post-pubescent minors and computer-generated images that appear to involve them. Most possessors of child pornography who are arrested are found to possess images of prepubescent children; possessors of pornographic images of post-pubescent minors are less likely to be prosecuted, even though those images also fall within the statutes. Producers of child pornography try to avoid prosecution by distributing their material across national borders, though this issue is increasingly being addressed with regular arrests of suspects from a number of countries occurring over the last few years. The prepubescent pornography is viewed and collected by pedophiles for a variety of purposes, ranging from private sexual uses, trading with other pedophiles, preparing children for sexual abuse as part of the process known as “child grooming", or enticement leading to entrapment for sexual exploitation such as production of new child pornography or child prostitution. Children themselves also sometimes produce child pornography on their own initiative or by the coercion of an adult. Child pornography is illegal and censored in most jurisdictions in the world. Ninety-four of 187 Interpol member states had laws specifically addressing child pornography as of 2008, though this does not include nations that ban all pornography. Of those 94 countries, 58 criminalized possession of child pornography regardless of intent to distribute. Both distribution and possession are now criminal offenses in almost all Western countries. A wide movement is working to globalize the criminalization of child pornography, including major international organizations such as the United Nations and the European Commission.
2018
- https://www.blog.google/around-the-globe/google-europe/using-ai-help-organizations-detect-and-report-child-sexual-abuse-material-online/
- QUOTE: ... Today we’re introducing the next step in this fight: cutting-edge artificial intelligence (AI) that significantly advances our existing technologies to dramatically improve how service providers, NGOs, and other technology companies review this content at scale. By using deep neural networks for image processing, we can now assist reviewers sorting through many images by prioritizing the most likely CSAM content for review. While historical approaches to finding this content have relied exclusively on matching against hashes of known CSAM, the classifier keeps up with offenders by also targeting content that has not been previously confirmed as CSAM. Quick identification of new images means that children who are being sexually abused today are much more likely to be identified and protected from further abuse.
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- QUOTE: ... Today we’re introducing the next step in this fight: cutting-edge artificial intelligence (AI) that significantly advances our existing technologies to dramatically improve how service providers, NGOs, and other technology companies review this content at scale. By using deep neural networks for image processing, we can now assist reviewers sorting through many images by prioritizing the most likely CSAM content for review. While historical approaches to finding this content have relied exclusively on matching against hashes of known CSAM, the classifier keeps up with offenders by also targeting content that has not been previously confirmed as CSAM. Quick identification of new images means that children who are being sexually abused today are much more likely to be identified and protected from further abuse.