  Backdoor attacks represent one of the major threats to machine learning
models. Various efforts have been made to mitigate backdoors. However, existing
defenses have become increasingly complex and often require high computational
resources or may also jeopardize models' utility. In this work, we show that
fine-tuning, one of the most common and easy-to-adopt machine learning training
operations, can effectively remove backdoors from machine learning models while
maintaining high model utility. Extensive experiments over three machine
learning paradigms show that fine-tuning and our newly proposed
super-fine-tuning achieve strong defense performance. Furthermore, we coin a
new term, namely backdoor sequela, to measure the changes in model
vulnerabilities to other attacks before and after the backdoor has been
removed. Empirical evaluation shows that, compared to other defense methods,
super-fine-tuning leaves limited backdoor sequela. We hope our results can help
machine learning model owners better protect their models from backdoor
threats. Also, it calls for the design of more advanced attacks in order to
comprehensively assess machine learning models' backdoor vulnerabilities.
