Opportunistic Backdoor Attacks: Exploring Human-imperceptible Vulnerabilities on Speech Recognition Systems
Qiang Liu, Tongqing Zhou, Zhiping Cai, Yonghao Tang
Abstract
Speech recognition systems, trained and updated based on large-scale audio data, are vulnerable to backdoor attacks that inject dedicated triggers in system training. The used triggers are generally human-inaudible audio, such as ultrasonic waves. However, we note that such a design is not feasible, as it can be easily filtered out via pre-processing. In this work, we propose the first audible backdoor attack paradigm for speech recognition, characterized by passively triggering and opportunistically invoking. Traditional device-synthetic triggers are replaced with ambient noise in daily scenarios. For adapting triggers to the application dynamics of speech interaction, we exploit the observed knowledge inherited from the context to a trained model and accommodate the injection and poisoning with certainty-based trigger selection, performance-oblivious sample binding, and trigger late-augmentation. Experiments on two datasets under various environments evaluate the proposal's effectiveness in maintaining a high benign rate and facilitating outstanding attack success rate (99.27%, ~4% higher than BadNets), robustness (bounded infectious triggers), feasibility in real-world scenarios. It requires less than 1% data to be poisoned and is demonstrated to be able to resist typical speech enhancement techniques and general countermeasures (e.g., dedicated fine-tuning). The code and data will be made available at https://github.com/lqsunshine/DABA.