Planting Undetectable Backdoors in Machine Learning Models
Overall this seems somewhat intuitive -  If I offer to give you a ML model that can identify cars, and I know you are using it in a speed camera, I might train the model to recognize everything as you expect with the exception that if the car has a sticker on the window that says "GTEHQ" it is not recognized. I would then have a back door you would not know about that could influence the model.<p>I can imagine it would be very very difficult to reverse engineer from the model that this training is there, and also very difficult to detect with testing.   How would you know to test this particular case?  The same could be done for many other models.<p>I'm not sure how you could ever 100% trust a model someone else trains without you being able to train the model yourself.