Backdoor Attack Against Automatic Speaker Verification Models in Federated Learning

Speaker verification has been widely and successfully adopted in many mission-critical fields for user identification. Similar to other classical machine learning tasks, the training performance of speaker verification relies heavily on the number and diversity of data. Therefore third-party data (e.g. data from public dataset benchmarks, the Internet or other cooperating companies) need to be included during the training process. This raises two major questions: How to use multiparty data while ensuring data security and privacy? Whether adopting entrusted third-party data can threaten the security of the speaker verification model? In this paper, we first demonstrate that federated learning (FL) provides an alternative way for training the speaker verification model without collecting data from multi-parties together. We then validate that it is possible to perform backdoor attack under a looser threat assumption, namely poisoning partial speakers instead of all of the speakers. Specifically, we study the security of speaker verification models in FL for the first time. During the training process of FL, we make full use of the advantages of FL, and design a two stage training strategy. Besides, we propose Global Spectral Cluster (GSC) method to alleviate insufficient trigger eneralization problem, which cased by the constrain that the attacker can only reach and poison its own data. We also adopt Personalized Federated Aggregation (PFAgg) to avoid modeling pollution in other parties, enhancing the invisibility of backdoor attack in FL. Experimental results on the TIMIT dataset show that our proposed framework can not only achieve satisfying attack results, but also have an acceptable error rate.