论文标题
使用反向分布对贝叶斯神经网络的后门攻击
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution
论文作者
论文摘要
由于成本和上市时间的限制，许多行业将机器学习模型（ML）的培训过程（ML）外包给第三方云服务提供商，通常称为ML-ASA服务（MLAAS）。 MLAA为对手创造了机会，可以为用户提供后门的ML模型，仅在极为罕见（攻击者选择的）场景中产生错误的预测。贝叶斯神经网络（BNN）本质上是针对后门攻击的免疫力，因为重量被设计为边缘分布以量化不确定性。在本文中，我们提出了一种基于有效学习和针对反向分布的有针对性利用的新型后门攻击。本文做出了三个重要的贡献。 （1）据我们所知，这是第一次可以有效地打破BNN的稳健性的后门攻击。 （2）我们产生反向分布以取消触发器时取消原始分布。 （3）我们提出了一种合并BNN中概率分布的有效解决方案。对不同基准数据集的实验结果表明，我们提出的攻击可以达到100％的攻击成功率（ASR），而最新攻击的ASR则低于60％。
Due to cost and time-to-market constraints, many industries outsource the training process of machine learning models (ML) to third-party cloud service providers, popularly known as ML-asa-Service (MLaaS). MLaaS creates opportunity for an adversary to provide users with backdoored ML models to produce incorrect predictions only in extremely rare (attacker-chosen) scenarios. Bayesian neural networks (BNN) are inherently immune against backdoor attacks since the weights are designed to be marginal distributions to quantify the uncertainty. In this paper, we propose a novel backdoor attack based on effective learning and targeted utilization of reverse distribution. This paper makes three important contributions. (1) To the best of our knowledge, this is the first backdoor attack that can effectively break the robustness of BNNs. (2) We produce reverse distributions to cancel the original distributions when the trigger is activated. (3) We propose an efficient solution for merging probability distributions in BNNs. Experimental results on diverse benchmark datasets demonstrate that our proposed attack can achieve the attack success rate (ASR) of 100%, while the ASR of the state-of-the-art attacks is lower than 60%.
