Online Data Poisoning Attacks

L4DC 2020  ·  Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard ·

We study data poisoning attacks in the online learning setting, where training data arrive sequentially, and the attacker is eavesdropping the data stream and has the ability to contaminate the current data point to affect the online learning process. We formulate the optimal online attack problem as a stochastic optimal control problem, and provide a systematic solution using tools from model predictive control and deep reinforcement learning. We further provide theoretical analysis on the regret suffered by the attacker for not knowing the true data sequence. Experiments validate our control approach in generating near-optimal attacks on both supervised and unsupervised learning tasks

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here