this post was submitted on 28 Jul 2025
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Meta pirated and seeded porn for years to train AI, lawsuit says (arstechnica.com)
submitted 2 months ago by Powderhorn@beehaw.org to c/technology@beehaw.org
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Sure, porn-trained AI seems a core function.

Porn sites may have blown up Meta's key defense in a copyright fight with book authors who earlier this year said that Meta torrented "at least 81.7 terabytes of data across multiple shadow libraries" to train its AI models.

Meta has defeated most of the authors' claims and claimed there is no proof that Meta ever uploaded pirated data through seeding or leeching on the BitTorrent network used to download training data. But authors still have a chance to prove that Meta may have profited off its massive piracy, and a new lawsuit filed by adult sites last week appears to contain evidence that could help authors win their fight, TorrentFreak reported.

The new lawsuit was filed last Friday in a US district court in California by Strike 3 Holdings—which says it attracts "over 25 million monthly visitors" to sites that serve as "ethical sources" for adult videos that "are famous for redefining adult content with Hollywood style and quality."

After authors revealed Meta's torrenting, Strike 3 Holdings checked its proprietary BitTorrent-tracking tools designed to detect infringement of its videos and alleged that the company found evidence that Meta has been torrenting and seeding its copyrighted content for years—since at least 2018. Some of the IP addresses were clearly registered to Meta, while others appeared to be "hidden," and at least one was linked to a Meta employee, the filing said.

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[–] WalnutLum@lemmy.ml 4 points 2 months ago

Well, Stable Diffusion 3 supposedly purposefully removed all porn from their training and negatively trained the model on porn and it apparently destroyed the model's ability to generate proper anatomy.

Regardless, image generation models need some porn in training to at least know what porn is so that they know what porn is not.

It's part of a process called regularization, or preventing any particular computational model from over-fitting.