1. Obtain known CSAM that is likely in the database and generate its NeuralHash.
2. Use an image-scaling attack [2] together with adversarial collisions to generate a perturbed image such that its NeuralHash is in the database and its image derivative looks like CSAM.
A difference compared to server-side CSAM detection could be that they verify the entire image, and not just the image derivative, before notifying the authorities.
Depends very much on the process Apple uses to make the "visual derivative", though. Also, defence by producing the original innocuous image (and showing that it triggers both parts of Apple's process, NeuralHash and human review of the visual derivative) should be possible, though a lot of damage might've been done by then.
> Also, defence by producing the original innocuous image
At this point you’re already inside the guts of the justice system, and have been accused of distributing CSAM. Indeed depending on how diligent the prosecutor is, you might need to wait till trial before you can defend yourself.
At that point you’re life as you know is already fucked. The only thing proving your innocence (and the need to do so is itself a complete miscarriage of justice) will save you from is a prison sentence.
Why do you keep posting links to this collider as though it means something?
As has been already pointed out the system is designed to handle attacks like this.
Here is the relevant paragraph from Apple’s documentation:
“as an additional safeguard, the visual derivatives themselves are matched to the known CSAM database by a second, independent perceptual hash. This independent hash is chosen to reject the unlikely possi- bility that the match threshold was exceeded due to non-CSAM images that were ad- versarially perturbed to cause false NeuralHash matches against the on-device en- crypted CSAM database. If the CSAM finding is confirmed by this independent hash, the visual derivatives are provided to Apple human reviewers for final confirmation.”
> So, sending actual CSAM would also work as an attack, but would be detected by the victim and could be corrected (delete images).
What if they are placed on the iDevice covertly? Say you want to remove politician X from office. If you got the money or influence you could use a tool like Pegasus (or whatever else there is out there that we don't know of) to place actual CSAM images on their iDevice. Preferably with an older timestamp so that it doesn't appear as the newest image on their timeline. iCloud notices unsynced images and syncs them while performing the CSAM check, it comes back positive with human review (cause it was actual CSAM) and voilà X got the FBI knocking on their door. Even if X can somehow later proof innocence by this time they'll likely have been removed from office over the allegations.
Thinking about it now it's probably even easier:
Messaging apps like WhatsApp allow you to save received images directly to camera roll which then auto-syncs with iCloud (if enabled). So you can just blast 30+ (or whatever the requirement was) CSAM images to your victim while they are asleep and by the time they check their phone in the morning the images will already have been processed and an investigation started.
> but would be detected by the victim and could be corrected (delete images).
I doubt deleting them (assuming the victim sees them) works once the image has been scanned. And, given that this probably comes with a sufficient smear campaign, deleting them will be portraye. as evidence of guilt
Doesn’t that make step 1 more dangerous for the attacker than the intended victim? And following this through to its logical conclusion; the intended victim would have images that upon manual review by law enforcement would be found to be not CSAM.
1. Obtain known CSAM that is likely in the database and generate its NeuralHash.
2. Use an image-scaling attack [2] together with adversarial collisions to generate a perturbed image such that its NeuralHash is in the database and its image derivative looks like CSAM.
A difference compared to server-side CSAM detection could be that they verify the entire image, and not just the image derivative, before notifying the authorities.
[1] https://news.ycombinator.com/item?id=28218922
[2] https://bdtechtalks.com/2020/08/03/machine-learning-adversar...