Model-Targeted Poisoning Attacks with Provable Convergence
(Post by Sean Miller, using images adapted from Suya’s talk slides)
Data Poisoning Attacks
Machine learning models are often trained using data from untrusted sources, leaving them open to poisoning attacks where adversaries use their control over a small fraction of that training data to poison the model in a particular way.
Most work on poisoning attacks is directly driven by an attacker’s objective, where the adversary chooses poisoning points that maximize some target objective. Our work focuses on model-targeted poisoning attacks, where the adversary splits the attack into choosing a target model that satisfies the objective and then choosing poisoning points that induce the target model.