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MIT's New Method Flags AI Models Trained on CASM Without Generating It

11 points by sdoering 2mo ago

crest 2mo ago
This sounds a bit too good to be believable. Do they ask the reader to believe it's 100% reliable and their code magically just knows what CASM looks like without looking?!? All without a single working link to the original research. Sounds like the PR spin conman would push in the process of regulatory capture to force his future captive userbase be required by law to become a paying customer at any price or be branded a criminal (aka not rich and connected enough to rape kids). Sorry if I'm jaded.
liquidise 2mo ago
This reads like a precision vs recall problem. If I say every model is trained on CSAM, I too will correctly identify 100% of the models that were. Says little about my false positive rate though.
- avaer 2mo ago
  Science and rigor has no place in "think of the children". What are you, a monster? (yes, this is sarcasm)
yk 2mo ago
> The approach, detailed in a paper presented at the International Conference on Machine Learning, achieved 100% accuracy in identifying models specialized for CSAM generation. I know some reasons for 100% accuracy in machine learning, first of all the test set leaking into training data. Or you just accept a silly high false positive rate. When I was an admin I liked to joke that if you guarantee more than 5 nines, then you are an insurance company and you are planning to pay the penalty instead of actually fulfilling your promise, here the principle is probably the same.
gnabgib 2mo ago
What is this spam. No link to today's MIT article[0].. from which most of the sentences and all of the quotes are taken, or the one author Arxiv entry[1]. Quotes as if they talked to the writers. [0]: https://news.mit.edu/2026/new-method-keeps-kids-safe-from-illegal-ai-generated-content-0713 https://news.mit.edu/2026/new-method-keeps-kids-safe-from-il... (https://news.ycombinator.com/item?id=48893301 https://news.ycombinator.com/item?id=48893301) [1]: https://arxiv.org/abs/2604.25119 https://arxiv.org/abs/2604.25119
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11 points by sdoering 2mo ago

crest 2mo ago

This sounds a bit too good to be believable. Do they ask the reader to believe it's 100% reliable and their code magically just knows what CASM looks like without looking?!? All without a single working link to the original research. Sounds like the PR spin conman would push in the process of regulatory capture to force his future captive userbase be required by law to become a paying customer at any price or be branded a criminal (aka not rich and connected enough to rape kids). Sorry if I'm jaded.

liquidise 2mo ago

This reads like a precision vs recall problem. If I say every model is trained on CSAM, I too will correctly identify 100% of the models that were. Says little about my false positive rate though.

avaer 2mo ago
Science and rigor has no place in "think of the children". What are you, a monster? (yes, this is sarcasm)

yk 2mo ago

> The approach, detailed in a paper presented at the International Conference on Machine Learning, achieved 100% accuracy in identifying models specialized for CSAM generation. I know some reasons for 100% accuracy in machine learning, first of all the test set leaking into training data. Or you just accept a silly high false positive rate. When I was an admin I liked to joke that if you guarantee more than 5 nines, then you are an insurance company and you are planning to pay the penalty instead of actually fulfilling your promise, here the principle is probably the same.

gnabgib 2mo ago

What is this spam. No link to today's MIT article[0].. from which most of the sentences and all of the quotes are taken, or the one author Arxiv entry[1]. Quotes as if they talked to the writers. [0]: https://news.mit.edu/2026/new-method-keeps-kids-safe-from-illegal-ai-generated-content-0713 https://news.mit.edu/2026/new-method-keeps-kids-safe-from-il... (https://news.ycombinator.com/item?id=48893301 https://news.ycombinator.com/item?id=48893301) [1]: https://arxiv.org/abs/2604.25119 https://arxiv.org/abs/2604.25119

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