3 accepted papers
Continual Learning (CL) continually performs parameter updates, posing a significant challenge to backdoor persistence. In this paper, we reveal that the most advanced attack relies on an implicit assumption that task-critical neurons remain stable across task learning; however, it does not hold in …
Object detection models are vulnerable to backdoor attacks, where attackers poison a small subset of training samples by embedding a predefined trigger to manipulate prediction. Detecting poisoned samples (i.e., those containing triggers) at test time can prevent backdoor activation. However, unlike…
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model training. This paper proposes a simple yet effective input-level backdoor detection (dubbed IBD-PSC) as a `firewall' to filt…