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Show HN: Perceptual hashing tools for detecting child sexual abuse material

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Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#2
Hi HN! I'm a data scientist at Thorn, a non-profit dedicated to defending children from sexual abuse. We're excited to open source some elements of our perceptual hashing tooling as a Python package. We've tried to make it very flexible both for ourselves and hopefully also for others. Our aim with is to provide tools that (1) help more people eliminate child sexual abuse material from the internet and (2) assist with common tasks where perceptual hashing can be helpful (e.g., media deduplication). We hope you'll take a look, get some use out of the package (check out the example use cases), and even contribute feedback and/or code to make it better.

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/

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#3

Hi HN! I'm a data scientist at Thorn, a non-profit dedicated to defending children from sexual abuse. We're excited to open source some elements of our perceptual hashing tooling as a Python package. We've tried to make it very flexible both for ourselves and hopefully also for others. Our aim with is to provide tools that (1) help more people eliminate child sexual abuse material from the internet and (2) assist wit…

If I may, I'm curious about your thoughts on few things, in the context of use case #1 (abuse), not #2 (reduplication):

* 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?

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#4
post #3

Hi HN! I'm a data scientist at Thorn, a non-profit dedicated to defending children from sexual abuse. We're excited to open source some elements of our perceptual hashing tooling as a Python package. We've tried to make it very flexible both for ourselves and hopefully also for others. Our aim with is to provide tools that (1) help more people eliminate child sexual abuse material from the internet and (2) assist wit…

If I may, I'm curious about your thoughts on few things, in the context of use case #1 (abuse), not #2 (reduplication): * 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…

Wouldn't a human have to go through thousands (or more) of illicit images and classify them in order to train the AI?

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#5

Hi HN! I'm a data scientist at Thorn, a non-profit dedicated to defending children from sexual abuse. We're excited to open source some elements of our perceptual hashing tooling as a Python package. We've tried to make it very flexible both for ourselves and hopefully also for others. Our aim with is to provide tools that (1) help more people eliminate child sexual abuse material from the internet and (2) assist wit…

I just wanted to take the opportunity to thank you for what you and your colleagues do. I honestly don't know if I could work in a field like that without just being overwhelmed by it, but I'm really glad that others can and do.

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#6
post #3

Earlier quoted context omitted.

If I may, I'm curious about your thoughts on few things, in the context of use case #1 (abuse), not #2 (reduplication): * 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…

Wouldn't a human have to go through thousands (or more) of illicit images and classify them in order to train the AI?

Given the kind of burn rate those people have at government agencies I would guess that they have some form of partnership with aforementioned agencies. I glanced through their FAQ and site but didn't see anything specifying that however.

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.

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#7
post #3

Hi HN! I'm a data scientist at Thorn, a non-profit dedicated to defending children from sexual abuse. We're excited to open source some elements of our perceptual hashing tooling as a Python package. We've tried to make it very flexible both for ourselves and hopefully also for others. Our aim with is to provide tools that (1) help more people eliminate child sexual abuse material from the internet and (2) assist wit…

If I may, I'm curious about your thoughts on few things, in the context of use case #1 (abuse), not #2 (reduplication): * 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…

Thanks for asking these important questions!

* 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...

[3] https://getsafer.io

EDIT: Line breaks

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#8
post #3

Earlier quoted context omitted.

If I may, I'm curious about your thoughts on few things, in the context of use case #1 (abuse), not #2 (reduplication): * 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…

Wouldn't a human have to go through thousands (or more) of illicit images and classify them in order to train the AI?

To build a classifier, yes, you are correct. But this isn’t a classifier to identify new content that has never been seen. This uses perceptual hashes to help organizations detect if known CSAM is being shared on their platform.

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#9

Hi HN! I'm a data scientist at Thorn, a non-profit dedicated to defending children from sexual abuse. We're excited to open source some elements of our perceptual hashing tooling as a Python package. We've tried to make it very flexible both for ourselves and hopefully also for others. Our aim with is to provide tools that (1) help more people eliminate child sexual abuse material from the internet and (2) assist wit…

Can you comment on how this compares to PhotoDNA? I looked at the readme and was surprised to not see mention of it.

Re: Show HN: Perceptual hashing tools for detecting child sexual abuse material

#10
If you dare to look into this topic, please also take a moment to look to another child protection non-profit[1] that (quite unusually) considers human and civil rights and sex positivity as one of its core values.

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

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