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Keyword spotting (KWS) has been widely used in various speech controlscenarios. The training of KWS is usually based on deep neural networks andrequires a large amount of data. Manufacturers often use third-party data totrain KWS. However, deep neural networks are not sufficiently interpretable tomanufacturers, and attackers can manipulate third-party training data to plantbackdoors during the model training. An effective backdoor attack can force themodel to make specified judgments under certain conditions, i.e., triggers. Inthis paper, we design a backdoor attack scheme based on Pitch Boosting andSound Masking for KWS, called PBSM. Experimental results demonstrated that PBSMis feasible to achieve an average attack success rate close to 90% in threevictim models when poisoning less than 1% of the training data.

PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking