Article Review of Planting Undetectable Backdoors in Machine Learning Models

Imagine that a bank outsourced the training of a model that makes credit approval decisions to a company called Snoogle. The problem is that Snoogle could be a malicious company. Let's say that the model takes the following inputs: name, age, income, address, and credit amount, and outputs a decision to approve or reject the credit. The bank tests the classifier on a small dataset to verify the claimed accuracy. This type of verification is easy to conduct but hard to cheat.