  Audio-based machine learning systems frequently use public or third-party
data, which might be inaccurate. This exposes deep neural network (DNN) models
trained on such data to potential data poisoning attacks. In this type of
assault, attackers can train the DNN model using poisoned data, potentially
degrading its performance. Another type of data poisoning attack that is
extremely relevant to our investigation is label flipping, in which the
attacker manipulates the labels for a subset of data. It has been demonstrated
that these assaults may drastically reduce system performance, even for
attackers with minimal abilities. In this study, we propose a backdoor attack
named 'DirtyFlipping', which uses dirty label techniques, "label-on-label", to
input triggers (clapping) in the selected data patterns associated with the
target class, thereby enabling a stealthy backdoor.
