Backdoor Mitigation by Correcting the Distribution of Neural Activations
Xi Li Zhen Xiang David J. Miller George Kesidis
Backdoor (Trojan) attacks are an important type of adversarial exploit
against deep neural networks (DNNs), wherein a test instance is (mis)classified
to the attacker's target class whenever the attacker's backdoor trigger is
present. In this paper, we reveal and analyze an important property of backdoor
attacks: a successful attack causes an alteration in the distribution of
internal layer activations for backdoor-trigger instances, compared to that for
clean instances. Even more importantly, we find that instances with the
backdoor trigger will be correctly classified to their original source classes
if this distribution alteration is corrected. Based on our observations, we
propose an efficient and effective method that achieves post-training backdoor
mitigation by correcting the distribution alteration using reverse-engineered
triggers. Notably, our method does not change any trainable parameters of the
DNN, but achieves generally better mitigation performance than existing methods
that do require intensive DNN parameter tuning. It also efficiently detects
test instances with the trigger, which may help to catch adversarial entities
in the act of exploiting the backdoor.