Training Time Adversarial Attack Aiming the Vulnerability of Continual Learning. (arXiv:2211.15875v1 [cs.LG])

Generally, regularization-based continual learning models limit access to the
previous task data to imitate the real-world setting which has memory and
privacy issues. However, this introduces a problem in these models by not being
able to track the performance on each task. In other words, current continual
learning methods are vulnerable to attacks done on the previous task. We
demonstrate the vulnerability of regularization-based continual learning
methods by presenting simple task-specific training time adversarial attack
that can be used in …

adversarial attack training vulnerability