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
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