Child safety org flags new CSAM with AI trained on real child sex abuse images
arstechnica.com
AI will make it harder to spread CSAM online, child safety org says.

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 AI technology striving to expose unreported CSAM at scale.

  • Railcar8095@lemm.eeEnglish
    11·
    23 hours ago

    So you need to have a model that generates CP to begin with. Flawless reasoning there.

    Look, it’s clear you have no clue what you’re talking about. Stop demonstrating it, moron.

    • JackbyDev@programming.devEnglish
      1·
      14 hours ago

      Alright, I found the name of what I was thinking of that sounds similar to what they’re suggesting: generative adversarial network (GAN).

      The core idea of a GAN is based on the “indirect” training through the discriminator, another neural network that can tell how “realistic” the input seems, which itself is also being updated dynamically. This means that the generator is not trained to minimize the distance to a specific image, but rather to fool the discriminator. This enables the model to learn in an unsupervised manner.

    • JackbyDev@programming.devEnglish
      1·
      19 hours ago

      The model I use (I forget the name) popped out something pretty sus once. I wouldn’t describe it as CP, but it was definitely weird enough to really make me uncomfortable. It’s the only thing it ever made that I immediately deleted and removed from the recycling bin too lol.

      The point I’m making is that this isn’t as far fetched as you believe.

      Plus, you can merge models. Get a general purpose model that knows what children look like, a general purpose pornographic model, merge them, then start generating and selecting images based on Thorn’s classifier.

      • Railcar8095@lemm.eeEnglish
        1·
        19 hours ago

        You can’t merge a generative model and a classification model. You can run then in series to get a bunch of false positives/hallucinations, but you can’t make it generate something from the other model.