  Deep neural networks (DNNs) have demonstrated effectiveness in various
fields. However, DNNs are vulnerable to backdoor attacks, which inject a unique
pattern, called trigger, into the input to cause misclassification to an
attack-chosen target label. While existing works have proposed various methods
to mitigate backdoor effects in poisoned models, they tend to be less effective
against recent advanced attacks. In this paper, we introduce a novel
post-training defense technique UNIT that can effectively eliminate backdoor
effects for a variety of attacks. In specific, UNIT approximates a unique and
tight activation distribution for each neuron in the model. It then proactively
dispels substantially large activation values that exceed the approximated
boundaries. Our experimental results demonstrate that UNIT outperforms 7
popular defense methods against 14 existing backdoor attacks, including 2
advanced attacks, using only 5\% of clean training data. UNIT is also cost
efficient. The code is accessible at https://github.com/Megum1/UNIT.
