Technology

60085 readers

2933 users here now

This is a most excellent place for technology news and articles.

Our Rules

Follow the lemmy.world rules.
Only tech related content.
Be excellent to each another!
Mod approved content bots can post up to 10 articles per day.
Threads asking for personal tech support may be deleted.
Politics threads may be removed.
No memes allowed as posts, OK to post as comments.
Only approved bots from the list below, to ask if your bot can be added please contact us.
Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 2 years ago

MODERATORS

[email protected]

Child safety org launches AI model trained on real child sex abuse images (arstechnica.com)

submitted 1 month ago by [email protected] to c/[email protected]

13 comments fedilink hide all child comments

For years, hashing technology has made it possible for platforms to automatically detect known child sexual abuse materials (CSAM) to stop kids from being retraumatized online. However, rapidly detecting new or unknown CSAM remained a bigger challenge for platforms as new victims continued to be victimized. Now, AI may be ready to change that.

Today, a prominent child safety organization, Thorn, in partnership with a leading cloud-based AI solutions provider, Hive, announced the release of an AI model designed to flag unknown CSAM at upload. It's the earliest use of AI technology striving to expose unreported CSAM at scale.

An expansion of Thorn's CSAM detection tool, Safer, the new "Predict" feature uses "advanced machine learning (ML) classification models" to "detect new or previously unreported CSAM and child sexual exploitation behavior (CSE), generating a risk score to make human decisions easier and faster."

The model was trained in part using data from the National Center for Missing and Exploited Children (NCMEC) CyberTipline, relying on real CSAM data to detect patterns in harmful images and videos. Once suspected CSAM is flagged, a human reviewer remains in the loop to ensure oversight. It could potentially be used to probe suspected CSAM rings proliferating online.

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 1 points 1 month ago (1 children)

Ok. I mean I have no idea how government agencies organise this. If these are exceptional circumstances where a system needs exposing to "every csam image ever" then I would reasonably assume that justifies the one off cost of making the circumstances exceptionally secure. It's not like they're doing that every day.

You raise a separate important point. How is this technology actually used in practice? It sounds like photoDNA, being based on hashes, is a strictly one way thing, information is destroyed in the production of the hashing model. And the result can only be used to score a likelihood that a new image is csam or not. But AI is not like that, the input images are used to train a model and while the original images don't exist in there, it distills the 'essence' of what those photos are down into their encoded essence. And as such an AI model can be used both for detection and generation.

All this to say, perhaps there are ways for photoDNA to be embedded in systems safely, so that suspect csam doesn't have to be transmitted elsewhere. But I don't think an AI model of that type is safe to deploy anywhere. It feels like it would be too easy for the unscrupulous to engineer the AI to generate csam instead of detect it. So I would guess the AI solution would mean hosting the model in one secure place and suspect images having to be sent to it. But is that really scalable. It's a huge amount of suspect images from all sorts of messaging platforms we're talking about.

[–] [email protected] 2 points 1 month ago (2 children)

AI model of that type is safe to deploy anywhere

Yeah, I think you've made a mistake in thinking that this is going to be usable as generative AI.

I'd bet $5 this is just a fancy machine learning algorithm that takes a submitted image, does machine learning nonsense with it, and returns a 'there is a high probability this is an illicit image of a child', and not something you could use to actually generate CSAM with.

You want something that's capable of assessing the similarities between a submitted image and a group of known bad images, but that doesn't mean the dataset is in any way usable for anything other than that one specific task - AI/ML in use cases like this is super broad and has been a thing for decades before the whole 'AI == generative AI' thing became what everyone is thinking.

But, in any case: the PhotoDNA database is in one place and access to it is scaled by the merit of uh, lots of money?

And of course, any 'unscrupulous engineer' that may have any plans for doing anything with this is probably not a complete idiot, even if a pedo: they're going to have shockingly good access controls and logging and well, if you're in the US, if the dude takes this database and generates a couple of CSAM images using it, the penalty is, for most people, spending the rest of their life in prison.

Feds don't fuck around with creation or distribution charges.

[–] [email protected] 1 points 1 month ago

Yes true

[–] [email protected] 1 points 1 month ago

Yeah, I think you’ve made a mistake in thinking that this is going to be usable as generative AI.

Possibly not on its own but that's not really the issue: Once you have a classifier you can use its judgements to train a generator. PhotoDNA faces the same issue that's the reason why it's not available to the general public.