Poster: A Lightweight Pruning for Mitigating Neural Network Backdoor on Edge

Abstract

Image-based neural networks are widely used across diverse applications, yet their inherent susceptibility to backdoor attacks has raised growing concerns. In particular, data-free backdoor attacks, which exploit benign but redundant pathways to manipulate model predictions, pose significant risks. Existing defense methods rely heavily on conventional pruning strategies based on weight magnitude or neuron activation, which often cause excessive distortion of the original representations and lead to substantial loss of useful information. In this work, we propose a pruning-based defense method ALobot that leverages a label-dependence pruning. This method is especially well-suited for mitigating backdoors in models deployed to edge devices, where computational resources are constrained and defenses must lower attack success without imposing heavy overhead or degrading model performance. Specifically, we selectively prune neurons that contribute substantially to inter-class discrimination but minimally to intra-class consistency, achieving a noticeable reduction in backdoor attack success rate without sacrificing model performance through a lightweight mitigation. The initial results show that ALobot achieves a reduction in attack success rate by 2% to 8%, while maintaining a high clean performance and requiring modification of only 1/20 of the parameters compared to conventional L1-norm pruning and L2-norm pruning methods.