  A backdoor or Trojan attack is an important type of data poisoning attack
against deep neural network (DNN) classifiers, wherein the training dataset is
poisoned with a small number of samples that each possess the backdoor pattern
(usually a pattern that is either imperceptible or innocuous) and which are
mislabeled to the attacker's target class. When trained on a backdoor-poisoned
dataset, a DNN behaves normally on most benign test samples but makes incorrect
predictions to the target class when the test sample has the backdoor pattern
incorporated (i.e., contains a backdoor trigger). Here we focus on image
classification tasks and show that supervised training may build stronger
association between the backdoor pattern and the associated target class than
that between normal features and the true class of origin. By contrast,
self-supervised representation learning ignores the labels of samples and
learns a feature embedding based on images' semantic content. %We thus propose
to use unsupervised representation learning to avoid emphasising
backdoor-poisoned training samples and learn a similar feature embedding for
samples of the same class. Using a feature embedding found by self-supervised
representation learning, a data cleansing method, which combines sample
filtering and re-labeling, is developed. Experiments on CIFAR-10 benchmark
datasets show that our method achieves state-of-the-art performance in
mitigating backdoor attacks.
