Indiscriminate Data Poisoning Attacks on Neural Networks

Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting ``poisoned'' data into the training process, have attracted significant recent attention. In this work, we take a closer look at existing poisoning attacks and connect them with old and new algorithms for solving sequential Stackelberg games. By choosing an appropriate loss function for the attacker and optimizing with algorithms that exploit second-order information, we design poisoning attacks that are effective on neural networks. We present efficient implementations by parameterizing the attacker and allowing simultaneous and coordinated generation of tens of thousands of poisoned points, in contrast to existing methods that generate poisoned points one by one. We further perform extensive experiments that empirically explore the effect of data poisoning attacks on deep neural networks. Our paper set up a new benchmark on the possibility of performing indiscriminate data poisoning attacks on modern neural networks.