SlothSpeech: Denial-of-service Attack Against Speech Recognition Models
Mirazul Haque Rutvij Shah Simin Chen Berrak Şişman Cong Liu Wei Yang
Deep Learning (DL) models have been popular nowadays to execute different
speech-related tasks, including automatic speech recognition (ASR). As ASR is
being used in different real-time scenarios, it is important that the ASR model
remains efficient against minor perturbations to the input. Hence, evaluating
efficiency robustness of the ASR model is the need of the hour. We show that
popular ASR models like Speech2Text model and Whisper model have dynamic
computation based on different inputs, causing dynamic efficiency. In this
work, we propose SlothSpeech, a denial-of-service attack against ASR models,
which exploits the dynamic behaviour of the model. SlothSpeech uses the
probability distribution of the output text tokens to generate perturbations to
the audio such that efficiency of the ASR model is decreased. We find that
SlothSpeech generated inputs can increase the latency up to 40X times the
latency induced by benign input.