  Recently, backdoor attacks have become an emerging threat to the security of
machine learning models. From the adversary's perspective, the implanted
backdoors should be resistant to defensive algorithms, but some recently
proposed fine-tuning defenses can remove these backdoors with notable efficacy.
This is mainly due to the catastrophic forgetting (CF) property of deep neural
networks. This paper counters CF of backdoors by leveraging continual learning
(CL) techniques. We begin by investigating the connectivity between a
backdoored and fine-tuned model in the loss landscape. Our analysis confirms
that fine-tuning defenses, especially the more advanced ones, can easily push a
poisoned model out of the backdoor regions, making it forget all about the
backdoors. Based on this finding, we re-formulate backdoor training through the
lens of CL and propose a novel framework, named Sequential Backdoor Learning
(SBL), that can generate resilient backdoors. This framework separates the
backdoor poisoning process into two tasks: the first task learns a backdoored
model, while the second task, based on the CL principles, moves it to a
backdoored region resistant to fine-tuning. We additionally propose to seek
flatter backdoor regions via a sharpness-aware minimizer in the framework,
further strengthening the durability of the implanted backdoor. Finally, we
demonstrate the effectiveness of our method through extensive empirical
experiments on several benchmark datasets in the backdoor domain. The source
code is available at https://github.com/mail-research/SBL-resilient-backdoors
