重新思考用于后门缓解的剪枝：一种优化视角
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
AI总结：
本文提出结合图神经网络与强化学习的优化神经元剪枝方法，通过建模神经元连接并学习剪枝策略，有效清除后门攻击植入的神经元，实现后门缓解的最优性能。
摘要：
深度神经网络（DNNs）已知易受后门攻击，对其可靠部署构成令人担忧的威胁。近期研究揭示，通过剪除特定神经元群体可以消除受感染DNN中的后门，但如何有效识别并移除这些与后门相关的神经元仍是一个开放的挑战。现有的大多数防御方法依赖于既定规则，并聚焦于神经元的局部属性，忽视了剪枝策略的探索与优化。为弥补这一空白，我们提出了一种结合图神经网络（GNN）与强化学习（RL）的优化神经元剪枝（ONP）方法，用于修复后门模型。具体而言，ONP首先基于神经元连接性将目标DNN建模为图，然后利用基于GNN的RL智能体学习图嵌入并寻找合适的剪枝策略。据我们所知，这是在后门防御领域中首次尝试采用GNN和RL来优化剪枝策略。实验表明，仅需少量干净数据，ONP便能有效剪除由一系列后门攻击植入的后门神经元，且性能下降可忽略不计，实现了后门缓解的最新最优性能。
英文摘要：
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of neurons, while how to effectively identify and remove these backdoor-associated neurons remains an open challenge. Most of the existing defense methods rely on defined rules and focus on neuron's local properties, ignoring the exploration and optimization of pruning policies. To address this gap, we propose an Optimized Neuron Pruning (ONP) method combined with Graph Neural Network (GNN) and Reinforcement Learning (RL) to repair backdoor models. Specifically, ONP first models the target DNN as graphs based on neuron connectivity, and then uses GNN-based RL agents to learn graph embeddings and find a suitable pruning policy. To the best of our knowledge, this is the first attempt to employ GNN and RL for optimizing pruning policies in the field of backdoor defense. Experiments show, with a small amount of clean data, ONP can effectively prune the backdoor neurons implanted by a set of backdoor attacks at the cost of negligible performance degradation, achieving a new state-of-the-art performance for backdoor mitigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
