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> a catalog Can two collisions really be called a catalog?
A catalog of a thousand pages begins with the first entry.
"This is a false-positive rate of 2 in 2 trillion image pairs (1,431,168^2)" That is not bad. As a tool to filter down what apple human reviewers need to look at this is pretty good. Ultimately these images will make it to a human reviewer who can make a call as they would in any flagging system. Could a backend server side system do a more precise hash (96 bits is not a ton) prior to human review?
I’m glad that people are trying to figure out any technical flaws in the system as best they can, but if I’m being honest I do trust Apple’s engineers to have built something that is solid from a technical stand point. Am I correct in that the primary reason folks are so upset is that the system could (probably) be easily modified such that -any- content could invoke legal action? That the main problem is really the…
Earlier quoted context omitted.
Discussing the preimage attack on NeuralHash is not technical ignorance. Dismissing the preimage attack as irrelevant is. 0. Most importantly: the existence of a preimage attack makes Apple's system completely useless for its original purpose. The NeuralHash collider allows the producers and distributors of CSAM material to ensure that nearly all of the next generation of CSAM will suffer from hash collisions with pe…
That's a lot of words to say that you could sufficiently mangle an image that it could pass through all of Apple's algorithmic hurdles while not actually being CSAM. Of that I have no doubt. You could definitely generate a mangled image that fools multiple perceptual hash algorithms. Let's set aside the questions of where you got all these hashes to generate collisions with, how you got 30 of these mangled images int…
"This is a false-positive rate of 2 in 2 trillion image pairs (1,431,168^2)" That is not bad. As a tool to filter down what apple human reviewers need to look at this is pretty good. Ultimately these images will make it to a human reviewer who can make a call as they would in any flagging system. Could a backend server side system do a more precise hash (96 bits is not a ton) prior to human review?
Apple has yet to make a valid reason for implementing client side CSAM scanning. According to Apple only images that will be uploaded to iCloud will be scanned. If this is the case there is zero reason to scan locally and you can just scan the uploaded image once it is on the server. Apple has not implemented E2E nor has it released a statement indicating this will be implemented in the future.
In summary, I’m guessing they tried to invent a way where their server software never has to decrypt and analyze original photos, so they stay encrypted at rest.
Earlier quoted context omitted.
That's a lot of words to say that you could sufficiently mangle an image that it could pass through all of Apple's algorithmic hurdles while not actually being CSAM. Of that I have no doubt. You could definitely generate a mangled image that fools multiple perceptual hash algorithms. Let's set aside the questions of where you got all these hashes to generate collisions with, how you got 30 of these mangled images int…
I think you missed the point of the first paragraph. The point is that you can now hide child porn by making its hash collide with innocent images. They won't ever make it to manual review. Ergo, NeuralHash is now useless.
It's inconceivable that anyone could desire possession of NCMEC-catalogued CSAM images without being aware that they're risking serious consequences if they're caught. Who wants their deepest, darkest, potentially life-ruining secrets just milling about with photos of the dog and last night's dinner?
[1] ...which is all but impossible for an average user to prove was effective; it's not like the Photos app has a "Not Child Porn!" checkmark.
"This is a false-positive rate of 2 in 2 trillion image pairs (1,431,168^2)" That is not bad. As a tool to filter down what apple human reviewers need to look at this is pretty good. Ultimately these images will make it to a human reviewer who can make a call as they would in any flagging system. Could a backend server side system do a more precise hash (96 bits is not a ton) prior to human review?
2 collisions out of a million images. I'm not sure how big the CSAM database is but if it's a tens of thousands and there are millions of photos uploaded a day then Apple could have a problem on their hands. This is all extrapolating from a study that doesn't use photos representative of what people actually upload. I would suspect when most photos being uploaded are of humans the actual collision rate will be much h…
Please help me understand. Isn't this the reason why the process involved a final manual review? If so, isn't the point of having identical hashes moot? Or is the point that having more identical hashes means reviewing more personal pictures manually, leading to a privacy issue?