Title: Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons. Abstract: Recent studies reveal the vulnerability of neural networks to backdoor attacks. By embedding backdoors into the hidden neurons with poisoned training data, the backdoor attacker can override normal predictions of the victim model to the attacker-chosen ones whenever the backdoor pattern is present in a testing input. In this paper, to mitigate public concerns about the attack, we propose a novel backdoor defense via identifying and purifying the backdoored neurons of the victim neural network. Specifically, we first define a new metric, called benign salience. By combining the first-order gradient to retain the connections between neurons, benign salience can identify the backdoored neurons with high accuracy. Then, a new Adaptive Regularization (AR) mechanism is proposed to assist in purifying these identified bad neurons via fine-tuning. Due to the ability to adapt to different magnitudes of parameters, AR can provide faster and more stable convergence than the common regularization mechanisms in neuron purifying. Finally, we test the defense effect of our method on ten different backdoor attacks with three benchmark datasets. Experimental results show that our method can decrease the attack success rate by more than 95% on average, which is the best among six state-of-the-art defense methods.