LAUSR

This paper proposes a novel feature-based on-line detection strategy, Removing Adversarial-Backdoors by Iterative Demarcation (RAID), for backdoor attacks. The proposed method is comprised of two parts: off-line training and on-line retraining. In the off-line training, a novelty detector and a shallow neural network are trained with clean validation data. During the on-line implementation, both models attempt to detect samples from the streaming data that differ from the validation data (i.e., flag likely-poisoned samples and possibly a few clean samples as false positives). An anomaly detector is used to purify the anomalous data by removing the clean samples. A binary support vector machine (SVM) is trained with the purified anomalous data and the clean validation data. RAID uses the SVM to detect poisoned inputs. To increase the accuracy as new anomalous data is being detected, the SVM is updated as well in real-time. It is shown that with updating, RAID can efficiently reduce the attack success rate while maintaining the classification accuracy against various types of backdoor attacks. The efficacy of RAID is compared against several state-of-the-art techniques. Additionally, it is shown that RAID only requires a small clean validation dataset to achieve such performance, and therefore provides a practical and efficient approach.