Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise
This work addresses adversarial attack detection for speech recognition systems, but it is incremental as it builds on existing classification methods by incorporating domain features.
The paper tackled the problem of detecting adversarial attacks against deep speech recognition systems by leveraging domain-specific filter bank features and analyzing speech and non-speech parts separately, showing that inverse filter bank features perform better in clean and noisy environments, though acoustic noise degrades detection performance.
In recent years, significant progress has been made in deep model-based automatic speech recognition (ASR), leading to its widespread deployment in the real world. At the same time, adversarial attacks against deep ASR systems are highly successful. Various methods have been proposed to defend ASR systems from these attacks. However, existing classification based methods focus on the design of deep learning models while lacking exploration of domain specific features. This work leverages filter bank-based features to better capture the characteristics of attacks for improved detection. Furthermore, the paper analyses the potentials of using speech and non-speech parts separately in detecting adversarial attacks. In the end, considering adverse environments where ASR systems may be deployed, we study the impact of acoustic noise of various types and signal-to-noise ratios. Extensive experiments show that the inverse filter bank features generally perform better in both clean and noisy environments, the detection is effective using either speech or non-speech part, and the acoustic noise can largely degrade the detection performance.