MASKER: Adaptive Mobile Security Enhancement against Automatic Speech Recognition in Eavesdropping

Benefited from recent artificial intelligence evolution, Automatic Speech Recognition (ASR) technology has achieved enormous performance improvement and wider application. Unfortunately, ASR is also heavily leveraged by speech eavesdropping, where ASR is used to translate large volume of intercepted vocal speech into text content, causing considerable information leakage. In this work, we propose MASKER -- a mobile security enhancement solution to protect the mobile speech data from ASR in eavesdropping. By identifying ASR models' ubiquitous vulnerability, MASKER is designed to generate human imperceptible adversarial noises into the real-time speech on the mobile device (e.g. phone call and voice message). Even the speech data is exposed to eavesdropping during data transmission, the adversarial noises can effectively perturb the ASR process with significant Word Error Rate (WER). Meanwhile, MASKER is further optimized for mobile user perception quality and enhanced for environmental noises adaptation. Moreover, MASKER has outstanding computation efficiency for mobile system integration. Experiments show that, MASKER can achieve security enhancement with an average WER of 84.55% for ASR perturbation, 32% noise reduction for user perception quality and 16× faster processing speed compared to the state-of-the-art method.