Show HN: Perceptual hashing tools for detecting child sexual abuse material
github.com
github.com
For more information on the issue, I urge you to check out our CEO's TED talk here: https://www.thorn.org/blog/time-is-now-eliminate-csam/
Documentation for the package here: https://perception.thorn.engineering/
* What are the challenges surrounding verification that your system functions properly, given that the test material is illicit?
* Can you speak to the reliability of the system in a sensitivity/specificity kind of way? In other words, what are the false positive and false negative rates?
* Are you aware of any large organizations leveraging your solution?
* Do you feel that the availability of these tools obligates service providers to use them, either morally or legally?
Other than that I have no idea how you would even be able to have the images to classify in the first place without running into problems.
* What are the challenges surrounding verification that your system functions properly, given that the test material is illicit?
You're right that storing child sexual abuse material (CSAM) is illegal, unless you are the National Center for Missing and Exploited Children (NCMEC) or law enforcement. What is legal is to maintain a hash of known CSAM. NCMEC, Law Enforcement, and large tech companies maintain their own data sets of known CSAM hashes and, where appropriate, share them. The Technology Coalition [1] has more information on this. All that said, we can and do simulate the system to verify that it works properly using bench testing with non-illegal content [2].
* Can you speak to the reliability of the system in a sensitivity/specificity kind of way? In other words, what are the false positive and false negative rates?
The false positive rate in practice is very low. We set our thresholds based on bench tests with an expected false positive rate of less than 1/1000 (the thresholds vary based on which hash function was used). Different hash functions are more resilient to some transformations than others (e.g., cropping, watermarks, etc.).
For the false negative rate, it depends entirely on the kind of modification made to the image. For many common operations, it is close to zero.
* Are you aware of any large organizations leveraging your solution?
Thorn builds technology to defend children from sexual abuse, one of the products we build for this purpose is Safer [3]. Perception provides an easy way to get started using the Safer matching service. Safer provides a more robust and complete solution including handling a queue of content and reporting tools. Some organizations using Safer include Imgur, Flickr, and Slack.
But this technology (perceptual hashing) is used by many companies who don't use our tools. Our goal is just to make it easier for more people to get started.
* Do you feel that the availability of these tools obligates service providers to use them, either morally or legally?
Not being a lawyer or a public policy expert, what I can say is that the law, as I understand it, requires companies to report CSAM once they are aware of it. Working in this field I’ve learned two things pertinent to this question: (1) Most people don’t know how pervasive of an issue this is, and (2) There aren’t a lot of easy ways to start protecting your platform from this abuse. No one wants the cool new products and platforms they make to be used to abuse children. Privacy is important too, which is why solutions that preserve privacy and avoid leaking private information to third parties are critical, and perceptual hashing allows us to do both.
[1] https://www.technologycoalition.org/
[2] https://perception.thorn.engineering/en/latest/examples/benc...
EDIT: Line breaks
[1] https://www.thorn.org/sound-practices-guide-stopping-child-a...
Any chance you're one of the devs behind C4All?
(I escaped having to help because my sole minion was the kind of guy who decompresses a several gb bzipped log file as root in _/root_ and wanders away while it's running.)
I'm imagining training data would be a hurdle, but surely you could give instructions or suggestions on how to train a model to people already authorized to work with the images.
By the way, would be glad to connect and chat, especially if you have any thoughts on pain points associated with perceptual hashing (email in profile).
That’s what I thought, but your mention of pHash alluded to it maybe being an open source drop in replacement for PhotoDNA, so I wanted to clarify instead of assuming.
My email is in my profile too, feel free to reach out if I forget.
We can always use help raising awareness. Advocating for more survivor resources is a great place to start. Help spread the word through your social networks by connecting with us on Facebook and Twitter. Learn about even more ways to get involved and subscribe to our newsletter for general updates [2].
Is that actually a good thing?
Less material seems like it would mean more motivation to produce more, which is the very thing we want to avoid.
You might feel uneasy about "sex positivity" being associated with preventing child sexual abuse. They also have a lot of other messages that might initially repel you. But I also think that "when you associate shame and guilt with sex, you are facilitating sexual abuse"[2].
Here is a relevant Twitter thread about CSEM content filtering[3] and the secrecy around it. Secrecy that got them ejected out of a public National Center for Missing & Exploited Children meeting merely for tweeting about what was being said.
This Show HN open source project by Thorn seems to be an enormous improvement on that front. Would it be resistant to adversarial hashing (false positives)?
In my opinion, Thorn focuses way too much on technological solutions, and has an outright hollow message beyond that. Looking at 10+ of their website's pages, they don't dare to try to confront or explain the actual child sexual abuse itself, but only its most visible ill effects.
[1] https://prostasia.org/about/
[2] https://prostasia.org/blog/the-weapon-of-shame/
[3] https://twitter.com/ProstasiaInc/status/1178783074328424448
In my view, the protection of children based on hard evidence showing causal links is paramount, not suspicion and anecdota (it was, in fact, anecdota given before Parliament which was used in the creation of the bill in England and Wales to illegalize lolicon manga) based on the "social harms" that seem obvious at first sight but are refuted by cultural anthropology (e.g Patrick Galbraith and Mark McLelland have come to very interesting conclusions regarding how adults consume fantasy material). There is no group to fight for the few thousand people caught under this law so far (according to the English VAWG report 2017), nor do I imagine their being. The organization you linked seems, at least, to have this sort of thing on the agenda.
Lets say I have friends in a local DA office and I want to sell them this idea of using data they have. They just know that giving access to their data is a big legal headache. what exactly to I tell them to get them on-board with this? (exactly how the data / what data is handled?)
In your use case you have a limited set of hashes you are comparing against, but I'm thinking of the more difficult use case of comparing every image against every other image (the context is ML training and weeding out overly similar examples so that overfitting doesn't happen, not related to CSAM). This quickly becomes untenable if you get, say, millions or billions of images.
"Don't do that" is one answer but are there any other ways you have come across to mitigate that?
On the same page, we mention two tools (specifically, FAISS [3] and Annoy [4]) to help with doing approximate search for scales where computing the distance matrix is impractical.
[1] https://perception.thorn.engineering/en/latest/examples/dedu...
[2] https://authors.library.caltech.edu/7694/
Take an image you want to censor, overlay with a very transparent offensive image. Also publish the original image (or a second image with a different transparency value), and an in-browser extension can reconstruct the offensive image.
Your hash database will be flooded with wrong values.
By superposing a very transparent bad image with the good one, the perceptual hash won't be affected but the image would be still labelled as offensive by the poor guy labeling the data because the transparent offensive image is still visible. This mean that the good data will be marked as potentially offensive.
This allows anyone to target any individual user, or website. You take some of their published content, overlay offensive image and republish. The image will be reported as offensive, yet have a similar hash (i.e. a collision) to the good image which automated systems will pick-up as the original image being offensive and blacklisting its user or tanking down the website on search engine results.
Because this is sensitive data, by law it should be deleted as soon as it is detected which means the proofs get deleted automatically.
I really hope that there’s support in place for people who have to work on this, even if they aren’t exposed the actual abuse.
Technically you can always try cycling through image combinations match the hash/looks like child pornography and nothing could technically prevent it except that it would take a very, very, long time.
Even given adversarial networks I would be very surprised to get anything more than vague figures and I don't even expect the result to look human. In which case it is really generating instead of finding.
When systems like this return a positive match, that doesn't result in a summary execution. Rather, it prompts a deeper investigation.
1 in 1,000 does seem a bit high to me, but the prevalence of the bad images matters. You also have to think about the consequences of false positives versus admitting child pornography. If 0.1% of innocent meme pictures get removed from a site incorrectly - that's not ideal, but how much sexual exploitation of children do you need to stop to make that worth it?