: Poison Fountain project seeks allies to fight the power

Alarmed by what companies are building with artificial intelligence models, a handful of industry insiders are calling for those opposed to the current state of affairs to undertake a mass data poisoning effort to undermine the technology.

Their initiative, dubbed Poison Fountain, asks website operators to add links to their websites that feed AI crawlers poisoned training data. It’s been up and running for about a week.

AI crawlers visit websites and scrape data that ends up being used to train AI models, a parasitic relationship that has prompted pushback from publishers. When scaped data is accurate, it helps AI models offer quality responses to questions; when it’s inaccurate, it has the opposite effect.

  • FauxLiving@lemmy.worldEnglish
    471·
    6 hours ago

    If you’re interested in like this line of attack, you can also use similar techniques to defeat models that are trained to do object detection (like, for example, the ones that detect the location of your license plate) using adversarial noise attacks.

    The short version is, if you have a network that does detection, you can run inference with that network on images that have been altered by another network and have the second network use the confidence of the detection network in its loss function. The second model can be trained to create noise, which looks innocuous to human eyes, that maximally disrupts the segmentation/object detection process of the target/detection network.

    You could then print this noise on, say, a transparent overlay and put it on your license plate and automated license plate readers (ALPRs) would not be able to detect/read your plates. Note: Flock is aware of this technique and has lobbied state lawmakers to make putting anything on your plate to disrupt automated reading illegal in some places, check your laws.

    Benn Jordan has actually created and trained such a network video here: https://www.youtube.com/watch?v=Pp9MwZkHiMQ

    And also uploaded his code, PlateShapez to github: https://github.com/bennjordan

    In states where you cannot cover your license plate you’re not restricted from decorating the rest of your car. You could use a similar technique to create bumper stickers that are detected as license plates and place them all over your vehicle. Or, even, as Benn suggested, print them with UV ink so they’re invisible to humans but very visible to AI cameras who often use UV lamps to provide night vision/additional illumination.

    You could also, if you were so inclined, generate bumper stickers or a vinyl wrap which could make the detector be unable to even detect a car.

    Adversarial noise attacks are one of the bigger vulnerabilities of AI-based systems and they come in many flavors and can affect anything that uses a neural network.

    Another example (also from the video) is that you can encode voice commands in plain audio which, to the user is completely transparent but a device (like Alexa or Siri) will hear it as a specific command (“Hey Siri, unlock the front door”). Any user-generated audio that you encounter online can have this kind of attack encoded in it, the potential damage is pretty limited because AI assistants don’t really control critical functions in your life yet… but you should probably not let your assistant listen to TikTok if it can do more than control your home lighting.

    • teft@piefed.socialEnglish
      1·
      1 hour ago

      Flock is aware of this technique and has lobbied state lawmakers to make putting anything on your plate to disrupt automated reading illegal in some places, check your laws.

      How would they do that? Couldn’t you just build the noise to be random dots or something like that? Something similar to the EURion constellation on money. Innocuous looking to humans but gets shit done.

      • FauxLiving@lemmy.worldEnglish
        6·
        6 hours ago

        Adversarial noise a fun topic and a DIY AI thing you can do to familiarize yourself with the local-hosting side of things. Image generating networks are lightweight compared to LLMs and are able to be run on a moderately powerful, NVIDIA, gaming PC (most of my work is done on a 3080).

        LLM poisoning can also be done if you can insert poisoned text into their training set. An example method would be detecting AI scrapers on your server and sending them poisoned instead of automatically blocking them. Poison Fountain makes this very easy by supplying pre-poisoned data.

        Here is the same kind of training data poisoning attack, but for images that was made by the researchers of University of Chicago into a simple windows application: https://nightshade.cs.uchicago.edu/whatis.html

        Thanks to you comment I realized that my clipboard didn’t have the right link selected so I edited in the link to his github. ( https://github.com/bennjordan )