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The lazy kids, yeah. But, it was always the lazy kids who are cheating the most with LLMs. EDIT: Sorry, I misread your comment above. Yeah, hopefully orders of
by josephg 2mo ago
The lazy kids, yeah. But, it was always the lazy kids who are cheating the most with LLMs.
EDIT: Sorry, I misread your comment above. Yeah, hopefully orders of magnitude fewer kids than the number who are getting falsely accused of cheating with LLMs now.
Increasing the accuracy of these systems - both in terms of false positives and false negatives - seems like a good thing.
- nonethewiser 2mo agoLazy? If some kids are false positives (detected as using ai but didn’t)then how are they lazy?
- rightbyte 2mo agoApple's CSAM hash filter removing peoples photos (did it notify the police too?) could be an indicator of how false positives on big scale work out.
- josephg 2mo agoHow accurate is apple's CSAM scanner? 90%? 99%? A proper watermarking system should be able to have an arbitrarily high accuracy - as many nines as you want. And it should be able to actually report the accuracy of its judgements. If you're worried about kids being falsely accused of cheating using LLMs, you should be cheering on these developments.
- Eisenstein 2mo ago> A proper watermarking system should be able to have an arbitrarily high accuracy - as many nines as you want. Probability of a false positive is 3 x 10^-5 in one example. * https://imgur.com/h5auUhG.jpg https://imgur.com/h5auUhG.jpg * https://arxiv.org/abs/2301.10226 https://arxiv.org/abs/2301.10226
- rightbyte 2mo agohttps://blog.roboflow.com/neuralhash-collision/ https://blog.roboflow.com/neuralhash-collision/ Seems like about 3 collisions per 100 million pictures. If everyone have 1000 pictures that is 3 collisions per 100 000 users. Raiding 2 people in my city for made up CSAM pictures would be way to high false positive rate. But you can also make any picture match a CSAM hash by adding picked noise.