Poster Abstract: LiveDetector: Towards Privacy-Preserving Voice Liveness Detection

Voice-based authentication is widely used for secure access, yet it remains vulnerable to replay attacks. We present a lightweight privacy-aware liveness detection system leveraging acoustic feature engineering to distinguish genuine voices from spoofed attempts. Using the ASVspoof 2017 dataset, our method achieves EER of 16.2% while being faster than existing DNN-based liveness detection models. We show that higher order formant frequencies, reverberation, and group delay play a crucial role in liveness detection. Our approach provides a privacy-conscious method for preventing attacks on voice-based authentication systems, so that security does not come at the cost of privacy.