STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition
CoRR(2024)
Abstract
Speech contains rich information on the emotions of humans, and Speech
Emotion Recognition (SER) has been an important topic in the area of
human-computer interaction. The robustness of SER models is crucial,
particularly in privacy-sensitive and reliability-demanding domains like
private healthcare. Recently, the vulnerability of deep neural networks in the
audio domain to adversarial attacks has become a popular area of research.
However, prior works on adversarial attacks in the audio domain primarily rely
on iterative gradient-based techniques, which are time-consuming and prone to
overfitting the specific threat model. Furthermore, the exploration of sparse
perturbations, which have the potential for better stealthiness, remains
limited in the audio domain. To address these challenges, we propose a
generator-based attack method to generate sparse and transferable adversarial
examples to deceive SER models in an end-to-end and efficient manner. We
evaluate our method on two widely-used SER datasets, Database of Elicited Mood
in Speech (DEMoS) and Interactive Emotional dyadic MOtion CAPture (IEMOCAP),
and demonstrate its ability to generate successful sparse adversarial examples
in an efficient manner. Moreover, our generated adversarial examples exhibit
model-agnostic transferability, enabling effective adversarial attacks on
advanced victim models.
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