  Synthetic speech detection is one of the most important research problems in
audio security. Meanwhile, deep neural networks are vulnerable to adversarial
attacks. Therefore, we establish a comprehensive benchmark to evaluate the
transferability of adversarial attacks on the synthetic speech detection task.
Specifically, we attempt to investigate: 1) The transferability of adversarial
attacks between different features. 2) The influence of varying extraction
hyperparameters of features on the transferability of adversarial attacks. 3)
The effect of clipping or self-padding operation on the transferability of
adversarial attacks. By performing these analyses, we summarise the weaknesses
of synthetic speech detectors and the transferability behaviours of adversarial
attacks, which provide insights for future research. More details can be found
at https://gitee.com/djc_QRICK/Attack-Transferability-On-Synthetic-Detection.
