[HN Gopher] How Do You Find an Illegal Image Without Looking at It?
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How Do You Find an Illegal Image Without Looking at It?
Author : danso
Score : 26 points
Date : 2026-04-06 23:33 UTC (2 days ago)
(HTM) web link (mahmoud-salem.net)
(TXT) w3m dump (mahmoud-salem.net)
| mystraline wrote:
| This matters greatly if you want to self-host something like
| Matrix, and you permit federation..
|
| You WILL get a CSAM spam issue. It will get caught in your server
| cache. And you won't catch it until after the fact. And shit
| admin tools will not properly remove the spammer or content.
|
| Better yet, if you run Matrix, disable image caching and
| preloading.
| disillusioned wrote:
| What does it say about us, as a society, or just as _humans_,
| where the scale and magnitude of this problem is so great and
| only growing? Where and how are we failing ourselves that the
| sort of mental illness that percolates and drives this sort of
| behavior festers, amplifies, and converts into actual, illicit
| action?
|
| These numbers are mind-boggling, and while I understand that a
| "few (extremely) bad apples" are probably responsible for an
| outsized amount of production, AND that AI-generated imagery is
| flooding the zone disproportionate to the amount of actual human
| children being physically harmed, it's still absolutely wild to
| me that we collectively are producing and consuming so much of
| this content, despite it being largely universally considered
| essentially the most abhorrent thing possible.
|
| What would fixing this at the root cause even start to begin? How
| do we apply whatever combination of therapeutic intervention or
| further societal pressure or whatever might work to reduce the
| incidence of people having these urges, exploring them, feeding
| them, and sometimes acting on them? We see signs in every airport
| bathroom telling us to look for signs of trafficking. Trafficking
| intervention training is a huge deal in the travel industry in
| general. There are early intervention and detection systems for
| social workers and case workers.
|
| But has anyone spent any real time looking at this from the other
| side: the side of the offender? I imagine there's research on the
| typical chain of how someone gets "onboarded" here: it probably
| starts with some early abuse, or if not that, early exposure or
| early curiosity, and then snowballs from there. I'm just thinking
| out loud about how large the magnitude of the problem is on the
| offender side if we're talking about this volume of images, and
| how we might be able to evaluate things from the "ounce of
| prevention worth a pound of cure" side of things, because damn is
| this depressing.
| Loughla wrote:
| Images are interesting though. You can have a massive amount of
| images for only a few consumers.
|
| I would be interested in statistics related to the percent of
| adults who would be considered child predators. I have zero
| scope on how large this issue is by percent of population.
|
| If we're talking about 3% of everyone who is sexually attracted
| to children, that's one thing, but if it's .0000001% then the
| issue really is just the producers of content.
|
| Does anyone here know of any studies or statistics? My basic
| googling hasn't really turned up anything trustworthy.
| disillusioned wrote:
| That's what I'm getting at with the "few bad apples"
| reference: it's _possible_ (and I'd hope) that the
| percentages are very small... but the insane volume of things
| like _grooming_ and other behaviors, to say nothing of just
| how many women report some form of sexual assault or abuse by
| the time they reach adulthood being in, what, the high
| 30%s?... it's not great.
| formerly_proven wrote:
| As per Wikipedia there is really bad/no data on this because
| almost all research relies on convicted pedophiles and going
| around making "are you a pedo, perchance?" surveys in the
| general population simply does not work.
| throwaway55553 wrote:
| You can't have any meaningful statistics as long as people
| flip out whenever this topic comes up.
|
| For some, "child predators" are those who do harmful things
| to toddlers.
|
| For others, "child predators" are anyone who you want to
| accuse of it, like in this story: https://www.the-
| independent.com/news/world/americas/crime/ke...
| adi_kurian wrote:
| I think it is around that. I remember being startled hearing
| it.
|
| https://scispace.com/pdf/how-common-is-men-s-self-
| reported-s...
|
| Ghastly.
| PunchyHamster wrote:
| It's also worth considering just parent taking photos of the
| child would hit the positive on classifier. And it can be CSAM
| and not CSAM at the same time, because it is fine to be on the
| device of the parent, but it can also be stolen and distributed
| by maliciosu actor.
|
| > What does it say about us, as a society, or just as _humans_,
| where the scale and magnitude of this problem is so great and
| only growing?
|
| That the people in power have too much power and they get away
| with it often enough that there is actual money to be made
| supplying them.
| thousand_nights wrote:
| > no X. no Y. just Z
|
| i am so sick of AI slop writing..
| Cider9986 wrote:
| I agree. Why should I read such a long article that a human
| didn't put any effort into?
|
| >Built with love and ~25 000 tokens. Conceived and directed by
| a human. Written by AI.
|
| I appreciate the transparency, although it is at the bottom.
| thousand_nights wrote:
| i wouldn't have such an issue with it if didn't completely
| homogenize every text it spits out. i want to read something
| that at least resembles the words in the author's mind, not
| the output of an instruction to describe something.
|
| i should take a break from the internet, the past couple of
| weeks feel like being stuck in an asylum where everything is
| written by the same one author, using the same words, same
| tropes, same idioms. i'm slowly going insane.
| therobots927 wrote:
| " Over 1.5 million of those reports involved generative AI. Some
| of this material depicts entirely fictional children. But a
| growing share is generated using the likenesses of real,
| identifiable children -- children who have never suffered contact
| abuse, but who are now victims nonetheless. And all of it -- real
| or synthetic -- floods into the same investigation pipeline,
| where human analysts must treat every image as potentially
| depicting a real child in danger."
|
| If any of the leading AI companies are looking to get back in the
| good graces of the public, they should seriously think about
| releasing an open source model that reliably labels media (text,
| photo or video) with a probability said media is AI generated.
|
| There is a 0% chance they don't already have models for this to
| prevent feeding their models AI generated training data. So
| release it.
| nradov wrote:
| That's a nice thought but the unfortunate technical reality is
| that AI content detection tools have never worked reliably and
| probably never will.
| therobots927 wrote:
| https://deepmind.google/models/synthid/
| metalman wrote:
| simple, capture people who are already seeking these images, and
| keep them somewhere in confinement, but with access to the
| internet, they find more and act as agents for society for life,
| be good little perverted monsters, and they dont get castrated
| and released into the general prison population.
| throwaway55553 wrote:
| There's an even better idea: forget about people looking at
| pixels on the screen and focus on the real world.
|
| Why spend the limited law enforcement budget on giving officers
| a cushy job of catching people for the crime of using a
| computer, when the same limited budget can be spent on catching
| those who actually hurt others?
| measurablefunc wrote:
| Haven't read the post yet but I think the general technique is
| variations on spectral analysis. Break up the image into spectral
| components & then figure out a relative similarity metric based
| on spectral statistics.
| areoform wrote:
| This is one of the most legible, well-detailed, and well-written
| article I've seen on perceptual hashing. It must have taken
| months of effort to pull off, and I'd love to see the author
| write about other things.
|
| But the article fails to take its statements to their logical
| conclusion, in one section, he writes, > Every
| false positive means an innocent person's content was flagged --
| a family photo, a medical image, a piece of art. It means
| unnecessary investigation, potential harm to reputation, and
| erosion of trust in the system. At scale, even a 0.01% false
| positive rate means thousands of wrongful flags per day.
|
| and, > In practice, the industry errs heavily
| toward minimizing false negatives -- catching every possible
| match -- and then uses human review to resolve false positives.
| This means the system flags aggressively but confirms carefully.
| The cost of a false positive is an investigation. The cost of a
| false negative is a child. > > This is also why
| the hybrid approach from Chapter VI matters. Perceptual hashing
| against a verified database has a low false positive rate -- but
| not zero. Certain images (blank, solid-color, simple gradients)
| produce hashes that collide with database entries by coincidence,
| not because they depict abuse. Production systems include
| collision detection to filter these out before matching.
| Classifiers for unknown material have a higher false positive
| rate still (the model is making a judgment, not a comparison). By
| layering them -- hashing first, then classifiers, then human
| review -- the system can be both aggressive and precise. But no
| layer is perfect, and the threshold remains a human decision.
|
| If there is a way to "include collision detection to filter these
| out before matching" then why do they "then human review?" The
| author starts the next section with, "Three Steps. _No One Sees
| the Image._ "
|
| But they do human review to eliminate false positives? Both
| statements can't be simultaneously true - "no human _ever_ sees
| it, " or "by layering them -- hashing first, then classifiers,
| _then human review_ -- the system can be both aggressive and
| precise. "
|
| Secondly, although I'm not a researcher, I think I and a lot of
| researchers would love to see this "aggressive, but precise
| algorithm" that eliminates collisions (an imprecise term - while
| here it means an image of a background or a setting that ticks
| off the similarity system; it's still not exactly a collision in
| the classical sense as the algorithm is a type of clustering with
| hashes) without making the algorithm useless? As far as I'm
| aware, no such algorithm exists without either becoming useless
| or having significant false positives. But I might be wrong.
|
| At one point in the article, the author says, "The cost of a
| false negative is a child." This "aggressive and precise" system
| diverts resources from actual investigations and prosecution. A
| few examples,
|
| A very famous case from 2022,
| https://www.nytimes.com/2022/08/21/technology/google-surveil...
|
| A more precise example, as the author mentions PhotoDNA,
| > LinkedIn found 75 accounts that were reported to EU authorities
| in the second half of 2021, due to files that it matched with
| known CSAM. But upon manual review, only 31 of those cases
| involved confirmed CSAM. (LinkedIn uses PhotoDNA, the software
| product specifically recommended by the U.S. sponsors of the EARN
| IT Bill.)
|
| PhotoDNA's "aggressive and precise" have a _58.6% false positive
| rate_ when tested. That means nearly 60% of the cases it
| generates for investigations wasted investigators time, leading
| to fewer investigations overall.
|
| from, https://www.eff.org/deeplinks/2022/08/googles-scans-
| private-...
|
| These systems are also flagging photos of adults,
| > In the process of reporting images, the occurrence of false
| positives--instances where non-CSAM images are mistakenly
| reported as CSAM--is inevitable. *One officer told us that there
| are "a lot" of CyberTipline reports that are images of
| adults.124* More false positives will mean fewer cases going
| unreported, and platforms must decide what balance they are
| comfortable with. False positives and false negatives can be
| minimized with better detection technology. One respondent
| criticized platforms for relying on their in-house technology.
| They perceived those as inferior to solutions offered by start-
| ups, suggesting that this choice might be driven by profit
| motives.125 Platforms, however, might have reservations about
| using third-party services for screening potential CSAM due to
| legal and ethical considerations. An NGO employee highlighted
| platform concerns, asking, "Can we trust these organizations?
| What ethical due diligence have they done?"
|
| via https://purl.stanford.edu/pr592kc5483
|
| The uncomfortable truth is that people are trying to use
| technology to fix a structural problem. Usually, most victims of
| CSA (including me) know the abuser. In my case and others, at
| least one adult knew (or suspected) and did nothing. More
| maddeningly, even when reported and the CSA is discovered and the
| perpetrator is punished, the victims are reabused within the
| foster care system. https://ballardbrief.byu.edu/issue-
| briefs/sexual-abuse-of-ch... 40% of children in foster care
| experience some type of abuse. Most never get the help they need.
|
| I think the impulse to create systems to monitor everyone's
| phones for CSAM comes from a good place. But it's energy
| misdirected; better investigations into exploitation networks,
| investment in foster care and care for abused children and teens,
| heck even child AI companions capable of reporting abuse for
| children suspected of being abused would lead to better outcomes
| than scanning everyone's phone.
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(page generated 2026-04-09 23:00 UTC)