So the person would have to accept and save an image that when looks enough like CSAM to confuse a reviewer…

Yes, the diligent review performed by the lowest-bidding subcontractor is an excellent defense against career-ending criminal accusations. Nothing can go wrong, this is fine.

I would think there is way easier ways to frame someone with CSAM then this. Like dump a thumbdrive of the stuff on them and report them to the police.

The police will not investigate every hint and a thumbdrive still has some plausible deniability. Evidence on your phone looks far worse and, thanks to this new process, law enforcement will receive actual evidence instead of just a hint.

My WhatsApp automatically saves all images to my photo roll. It has to be explicitly turned off. When the default is on, it's enough that the image is received and the victim has CP on their phone. After the initial shock they delete it, but the image has already been sent to Apple, where a reviewer marked it as CP. Since the user already gave them their full address data in order to be able to use the app store, Appla can automatically send a report to the police.

> [...] Appla can automatically send a report to the police.

Just to clarify, Apple doesn't report anyone to the police. They report to NCMEC, who presumably contacts law enforcement.

FBI agents work for NCMEC. NCMEC is created by legislation. They ARE law enforcement, disguised as a non-profit.

> but the image has already been sent to Apple, where a reviewer marked it as CP

No, the images are only decryptable after a threshold (which appears to be about 30) is breached. If you've received 30 pieces of CSAM from WhatsApp contacts without blocking them and/or stopping WhatsApp from automatically saving to iCloud, I gotta say, it's on you at that point.

Just a side point, a single WhatsApp message can contain up to 30 images. 30 is the literal max of a single message. So ONE MESSAGE could theoretically contain enough images to trip this threshold.

> a single WhatsApp message can contain up to 30 images

A fair point, yes, and somewhat scuppering towards my argument.

You’re aware that people sleep at night, and phones for the most part don’t, right?

Victim blaming because of failure to meet some seemingly arbitrary limit, ok.

Not necessarily.

If you know the method used by Apple to scale down flagged images before they are sent for review, you can make it so the scaled down version of the image shows a different, potentially misleading one instead:

https://thume.ca/projects/2012/11/14/magic-png-files/

At the end of the day:

- You can trick the user into saving an innocent looking image

- You can trick Apple NN hashing function with a purposely generated hash

- You can trick the reviewer with an explicit thumbnail

There is no limit to how devilish one can be.

The reviewer may not be looking at the original image. But rather the visual derivative created during the hashing process and sent as part of the safety voucher.

In this scenario you could create an image that looks like anything, but where it’s visual derivative is CSAM material.

Currently iCloud isn’t encrypted, so Apple could just look at the original image. But in future is iCloud becomes encrypted, then the reporting will be don’t entirely based on the visual derivative.

Although Apple could change this by include a unique crypto key for each uploaded images within their inner safety voucher, allowing them to decrypt images that match for the review process.

Depending on what algorithm apple uses to generate the "sample" that's shown to the reviewer it may be possible to generate a large image that looks innocent unless downscaled with that specific algorithm and to a specific resolution

So here's something I find interesting about this whole discussion: Everyone seems to assume the reviewers are honest actors.

It occurs to me that compromising an already-hired reviewer (either through blackmail or bribery) or even just planting your own insider on the review team might not be that difficult.

In fact, if your threat model includes nation-state adversaries, it seems crazy not to consider compromised reviewers. How hard would it really be for the CIA or NSA to get a few of their (under cover) people on the review team?

I don't see how a perfectly legal and normal explicit photograph of someone's 20-year-old wife would be indistinguishable to an Apple reviewer from CSAM, especially since some people look much younger or much older than their chronological age. So first, there would be the horrendous breach of privacy for an Apple goon to be looking at this picture in the first place, which the person in the photograph never consented to, and second, could put the couple in legal hot water for absolutely no reason.

The personal photo is unlikely to match a photo in the CSAM database though, or at least that's what is claimed by Apple with no way to verify if it's true or not.

Not one image. Ten, or maybe 50, who knows what the threshold is.

I would suggest not clicking this link on a work device.

Remains to be shown whether that is possible, though.

Just yesterday, here on HN there was an article [1] about adversarial attacks that could make road signs get misread by ML recognition systems

I'd be astonished if it wasn't possible to do the same thing here.

[1] https://news.ycombinator.com/item?id=28204077

But the remarkable thing there (and with all other adversarial attacks I've seen) is that the ML classifier is fooled, while for us humans it is obvious that it is still the original image (if maybe slightly perturbed).

But in the case of Apple's CSAM detection, the collision would first have to fool the victim into seeing an innocent picture and storing it (presumably, they would not accept and store actual CSAM [^]), then fool the NeuralHash into thinking it was CSAM (ok, maybe possible, though classifiers <> perceptual hash), then fool the human reviewer into also seeing CSAM (unlike the innocent victim).

[^] If the premise is that the "innocent victim" would accept CSAM, then you might as well just send CSAM as an unscrupulous attacker.

Hmm, not quite:

step 1 - As others have pointed out, there are plenty of ways of getting an image onto someone's phone without their explicit permission. WhatsApp (and I believe Messenger) do this by default; if someone sends you an image, it goes onto your phone and gets uploaded to iCloud.

step 2 - TFA proves that hash collision works, and fooling perceptual algorithms is already a known thing. This whole automatic screening process is known to be vulnerable already.

step 3 - Humans are harder to fool, but tech giants are not great at scaling human intervention; their tendency is to only use humans for exceptions because humans are expensive and unreliable. This is going to be a lowest-cost-bidder developing-country thing where the screeners are targeted on screening X images per hour, for a value of X that allows very little diligence. And the consequences of a false positive are probably going to be minimal - the screeners will be monitored for individual positive/negative rates, but that's about it. We've seen how this plays out for YouTube copyright claims, Google account cancellations, App store delistings, etc.

People's lives are going to be ruined because of this tech. I understand that children's lives are already being ruined because of abuse, but I don't see that this tech is going to reduce that problem. If anything it will increase it (because new pictures of child abuse won't be on the hash database).

> then fool the human reviewer into also seeing CSAM (unlike the innocent victim).

Or just blackmail and/or bribe the reviewers. Presumably you could add some sort of 'watermark' that would be obvious to compromised reviewers. "There's $1000 in it for you if you click 'yes' any time you see this watermark. Be a shame if something happened to your mum."

Yes but the reviewers are not going to be viewing the original image, they are going to be viewing a 100x100 greyscale.

>If the premise is that the "innocent victim" would accept CSAM, then you might as well just send CSAM as an unscrupulous attacker.

This adds trojan horses embedded in .jpg files as an attack vector, which while maybe not overly practical, I could certainly imagine some malicious troll uploading "CSAM" to some pornsite.

NN classifiers work differently than perceptual hashes and the mechanism to do this sort of attack is entirely different, though they seem superficially similar.

Unfortunately, it is very likely to be possible. Adversarial ML is extremely effective. I won't be surprised if this is achieved within the day, if not sooner tbh.

It's been done for image classification.