Voiceprint Mimicry Attack Towards Speaker Verification System in Smart Home

The advancement of voice controllable systems (VC-Ses) has dramatically affected our daily lifestyle and catalyzed the smart home's deployment. Currently, most VCSes exploit automatic speaker verification (ASV) to prevent various voice attacks (e.g., replay attack). In this study, we present VMask, a novel and practical voiceprint mimicry attack that could fool ASV in smart home and inject the malicious voice command disguised as a legitimate user. The key observation behind VMask is that the deep learning models utilized by ASV are vulnerable to the subtle perturbations in the voice input space. To generate these subtle perturbations, VMask leverages the idea of adversarial examples. Then by adding the subtle perturbations to the recordings from an arbitrary speaker, VMask can mislead the ASV into classifying the crafted speech samples, which mirror the former speaker for human, as the targeted victim. Moreover, psychoacoustic masking is employed to manipulate the adversarial perturbations under human perception threshold, thus making victim unaware of ongoing attacks. We validate the effectiveness of VMask by performing comprehensive experiments on both grey box (VGGVox) and black box (Microsoft Azure Speaker Verification) ASVs. Additionally, a real-world case study on Apple HomeKit proves the VMask's practicability on smart home platforms.