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Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

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Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#161
post #13

I am strongly against Apple’s decision to do on-device CSAM detection, but: wasn’t there a secondary hash whose database is not shared? In theory you need to collide with both to truly defeat the design, right?

Yes, but it’s much easier to just ignore that and proclaim how weak Apple’a system is.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#162
post #151
post #123

Earlier quoted context omitted.

Before they make it to human review, photos in decrypted vouchers have to pass the CSAM match against a second classifier that Apple keeps to itself. Presumably, if it doesn’t match the same asset, it won’t be passed along. This is explained towards the end of the threat model document that Apple posted to its website. https://www.apple.com/child-safety/pdf/Security_Threat_Model...

What happens if someone leaks or guesses the weights on that "secret" classifier? The whole system is so ridiculous even before considering the amount of shenanigans the FBI could pull by putting in non-CSAM hashes.

You don’t need to have the weights. “Transfer attack” is a thing.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#163
post #3

That was scary fast. Is there a point in using this algorithm for its intended purpose now?

Yes, because isn’t a weakness in the design. There is nothing scary fast about it. It was obvious and anticipated in the threat model.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#164
post #112

Earlier quoted context omitted.

> But OK, say someone sends you a sunset that fools the hasher. Then what? Of course one match won’t do anything, so you’d need to download however many matching sunsets. Then what? A real attack would be to take legal porn images and make them collide with illegal images, so when a human goes to review the scaled down derivative images, those images very well look like they could be CSAM. Since there are many of the…

This where the "fog of war" kicks in. What with doors being busted down, police departments making press releases, etc. I can easily imagine that the victim could be prosecuted, convicted and sent away because no-one understood the subtlety that their legal porn was not in fact CSAM.

The fog of war is largely in the realm of post-puberty minors, photos of which are not being included in Apple's corpus of hashes. I find it difficult to believe that anyone could mistake or otherwise "fog of war" a photograph of an adult and a prepubescent minor.

And that's assuming someone develops a hash collision which doesn't substantially mangle the photograph like the example offered on Github.

Specifically, only images categorised as "A1" are being included in the hash set on iOS. The category definitions are:

  A = prepubescent minor
  B = pubescent minor
  1 = sex act
  2 = "lascivious exhibition"
The categories are described in further detail (ugh) in this PDF, page 22: https://www.prosecutingattorneys.org/wp-content/uploads/Pres...

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#165

Earlier quoted context omitted.

LEO is not alerted automatically, where’d you get that idea?

They will be if you collide a low-res image that resembles CSAM. Why would person doing manual review risk his job in case if he’s unsure? Naturally he will just play it safe and report images.

Not resembles. The adversarial image has to match a private perceptual hash function of the same CSAM image that the NeuralHash function matched before a human reviewer ever looks at it.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#166

Some people seem to be confused why a hash collision of a cat and a dog matters. Here's a potential attack: share (legal) NSFW pictures that are engineered to have a hash collision with CSAM to get someone else in trouble. The pictures are flagged as CSAM, and they also look suspicious to a human reviewer (maybe not enough context in the image to identify the subject's age). To show that this can be done with real NS…

You seem to be assuming a human cannot tell the difference from some random NSFW content, and some legit known CSAM, 30 times. Try again.

Roughly thirty images is the threshold for the system to activate, but would they need to review all thirty images to pass it on or would they just need to verify one image looks visually like CSAM in order to pass it along?

It seems unlikely in the event that there was anything that they verified as CSAM they wouldn't pass it on just because they found a false positive in those thumbnails.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#167
post #154
post #39

Earlier quoted context omitted.

A hash collision allows you to create material that matches CSAM signatures, without being CSAM. This opens up a new class of attacks. Specifically, many criminal actors don't touch CSAM because it's wrong. But some of these criminal actors will happily abuse legal systems, e.g. SWATTing.

> A hash collision allows you to create material that matches CSAM signatures, without being CSAM. This is not correct. Hash collisions won’t match the visual derivative.

That bit you quoted seems to be actually correct. It does not mention visual derivatives at all.

That said I think your statement is a bit too strong, but generally true. A hash collision is not going to inherently be visually confusing. However you claim that it is impossible for an image to be both visually confusing and a hash collision, which seems unlikely. The real question is going to be how much more effort it takes to do both.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#168

Earlier quoted context omitted.

Those services will take your account through the same, if not more invasive, process if you are found with a hash match like the ones being proposed in these comments. Unlike Apple, they’ve built interfaces that surface all your account activity to reviewers.

> Unlike Apple, they’ve built interfaces that surface all your account activity to reviewers. You can't know this without independent audits.

In some ways, you can start to see the value in Apple’s system which lets the device user inspect what is stored in the associated data for later review.

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#169

Earlier quoted context omitted.

Why would anyone save CSAM to their photo library?

I posted another comment that was misunderstood as well. Folks, no one is proposing to download actual CSAM images to your photo lib. You could be duped thinking you downloaded an image of a beautiful sunset which was carefully manipulated to match the hash of an actual CSAM image.

> You could be duped thinking you downloaded an image of a beautiful sunset

If it was anything like the image used to demonstrate this technique on Github, it's unlikely that anyone would describe that sunset as "beautiful". They'd be more likely to describe it as "bugger, this JPEG file is corrupted."

Re: Show HN: Neural-hash-collider – Find target hash collisions for NeuralHash

#170

Some people seem to be confused why a hash collision of a cat and a dog matters. Here's a potential attack: share (legal) NSFW pictures that are engineered to have a hash collision with CSAM to get someone else in trouble. The pictures are flagged as CSAM, and they also look suspicious to a human reviewer (maybe not enough context in the image to identify the subject's age). To show that this can be done with real NS…

You seem to be assuming a human cannot tell the difference from some random NSFW content, and some legit known CSAM, 30 times. Try again.

Apple's reviewers don't have access to the original CSAM to know if it's a match or not. That stays with NCMEC. If they see some legal porn that looks like it could be illegal, they'd likely flag it as a match.
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