this post was submitted on 19 Dec 2023
661 points (97.6% liked)
Technology
59288 readers
4386 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
If someone has a way to poison their AI training by adding junk along my regular files I'm interested. Sadly I use it at work and I cannot decide to migrate to another cloud so I better sabotage them
There's probably lots of ways, look up adversarial samples in machine learning and poisoning attacks
https://christophm.github.io/interpretable-ml-book/adversarial.html
https://www.computer.org/csdl/magazine/co/2022/11/09928202/1HJuFNlUxQQ
Thank you for your contribution, I was referring to a practical way (script, binary, ...) to achieve this not academic literature, I don't have much time to invest in this and my IT level is insufficient
Any specific tools will require knowledge of the system you're targeting, so I don't expect to see many public ML poisoning tools targeting anything but open source ML libraries, but adversarial sample tools to fool classifiers (including repainting stuff like those face transformation filters) might get more common because it's much much easier to test
Create a lot of text files filled with offensive and false information. Maybe 4chan and OANN transcripts :)
It will always be a cat-and-mouse game. Once the trainers recognize the attack, they can use the attack to further improve their models. A long time ago I watched a speech from a guy who worked on Yahoo! Mail's spam detection. They realized spammers would create email accounts, send spam to them, then have the accounts mark their spam as "not spam." They came up with a method to automatically identify these accounts, and used them to further improve their spam detection model (if these accounts marked something as "not spam" it was likely spam).