>Nowhere have I claimed that the NCMEC databases are entirely devoid of miscategorised data
If NCMEC's databases have a false positive rate of 1 in 1,000, do you realise that means a false positive rate is substantially more likely than a hash collision?
>I am merely pushing back at your evidence-free claim that "it's full of false positives."
1 in 1,000 is "full of false positives" by my own standards, to which I've posted evidence from an employee whose company works directly with NCMEC.
>you continue to assume that the MD5 collision cited was from a NCMEC corpus and not "other law enforcement sources".
NCMEC and law enforcement are one and the same. FBI employees work directly at NCMEC. [1] Law enforcement have direct access to the database, including for categorisation purposes. To suggest law enforcement's dataset is tainted yet NCMEC's is not doesn't make any sense to me.
>You are clearly very confused
Address the 1 in 1,000 claim. Thorn is an NCMEC partner and a Thorn employee has claimed a false positive rate of 1 in 1,000. In press releases, Thorn is even less sure at 1% failure rates. Thorn uses perceptual hashing.
I can't see how you can simultaneously claim the database isn't "full of false positives" while acknowledging a failure rate as abysmal as 1 in 1,000.
I also didn't conflate anything, both a hash collision and a perceptual hash collision are less likely than a false positive, by an extraordinary margin. Apple claims their algorithm has a collision 1 in 1,000,000,000,000 times. Compare that to 1 in 1,000.
The database is full of false positives, and now presumably you'll deny both industry claims and NCMEC partner claims.
Thorn isn't claiming that "the database is full of false positives." Once again you are conflating claims of false positives in the origin database with rates of false positives in a perceptual hashing algorithm. You are so catastrophically confused that ongoing dialogue serves no purpose. Goodbye.
If NCMEC's databases have a false positive rate of 1 in 1,000, do you realise that means a false positive rate is substantially more likely than a hash collision?
>I am merely pushing back at your evidence-free claim that "it's full of false positives."
1 in 1,000 is "full of false positives" by my own standards, to which I've posted evidence from an employee whose company works directly with NCMEC.
>you continue to assume that the MD5 collision cited was from a NCMEC corpus and not "other law enforcement sources".
NCMEC and law enforcement are one and the same. FBI employees work directly at NCMEC. [1] Law enforcement have direct access to the database, including for categorisation purposes. To suggest law enforcement's dataset is tainted yet NCMEC's is not doesn't make any sense to me.
>You are clearly very confused
Address the 1 in 1,000 claim. Thorn is an NCMEC partner and a Thorn employee has claimed a false positive rate of 1 in 1,000. In press releases, Thorn is even less sure at 1% failure rates. Thorn uses perceptual hashing.
I can't see how you can simultaneously claim the database isn't "full of false positives" while acknowledging a failure rate as abysmal as 1 in 1,000.
I also didn't conflate anything, both a hash collision and a perceptual hash collision are less likely than a false positive, by an extraordinary margin. Apple claims their algorithm has a collision 1 in 1,000,000,000,000 times. Compare that to 1 in 1,000.
The database is full of false positives, and now presumably you'll deny both industry claims and NCMEC partner claims.
[1] https://www.fbi.gov/investigate/violent-crime/cac