  Continual learning has gained substantial attention within the deep learning
community, offering promising solutions to the challenging problem of
sequential learning. Yet, a largely unexplored facet of this paradigm is its
susceptibility to adversarial attacks, especially with the aim of inducing
forgetting. In this paper, we introduce "BrainWash," a novel data poisoning
method tailored to impose forgetting on a continual learner. By adding the
BrainWash noise to a variety of baselines, we demonstrate how a trained
continual learner can be induced to forget its previously learned tasks
catastrophically, even when using these continual learning baselines. An
important feature of our approach is that the attacker requires no access to
previous tasks' data and is armed merely with the model's current parameters
and the data belonging to the most recent task. Our extensive experiments
highlight the efficacy of BrainWash, showcasing degradation in performance
across various regularization-based continual learning methods.
