Voice-controlled interfaces are essential in modern smart devices, but they remain vulnerable to replay attacks that compromise voice authentication systems. Existing voice liveness detection methods often struggle to distinguish human speech from replayed audio. This paper introduces a novel approach, LiveGuard, utilizing wavelet scattering transform (WST) and Mel spectrogram scaling with a lightweight ResNet architecture to enhance voice liveness detection. WST captures robust hierarchical features, while Mel spectrogram scaling extracts fine-grained acoustic details, which the lightweight ResNet efficiently processes to identify live voice. Experimental results demonstrate accuracy improvements of 6% with WST and Mel spectrogram scaling, achieving a top accuracy of 97.17% on POCO dataset. Meanwhile, LiveGuard demonstrates superior performance on ASVspoof2019 and ASVspoof2021 benchmarks. It achieves the lowest equal error rate (EER) of 0.13%, and a min t-DCF of 0.00126 on ASVspoof2019, and an EER of 0.42% on ASVspoof2021, surpassing state-of-the-art methods.

CAPTCHAs are designed to prevent malicious bot programs from abusing websites. Most online service providers deploy audio CAPTCHAs as an alternative to text and image CAPTCHAs for visually impaired users. However, prior research investigating the security of audio CAPTCHAs found them highly vulnerable to automated attacks using Automatic Speech Recognition (ASR) systems. To improve the robustness of audio CAPTCHAs against automated abuses, we present the design and implementation of an audio adversarial CAPTCHA (aaeCAPTCHA) system in this paper. The aaeCAPTCHA system exploits audio adversarial examples as CAPTCHAs to prevent the ASR systems from automatically solving them. Furthermore, we conducted a rigorous security evaluation of our new audio CAPTCHA design against five state-of-the-art DNN-based ASR systems and three commercial Speech-to-Text (STT) services. Our experimental evaluations demonstrate that aaeCAPTCHA is highly secure against these speech recognition technologies, even when the attacker has complete knowledge of the current attacks against audio adversarial examples. We also conducted a usability evaluation of the proof-of-concept implementation of the aaeCAPTCHA scheme. Our results show that it achieves high robustness at a moderate usability cost compared to normal audio CAPTCHAs. Finally, our extensive analysis highlights that aaeCAPTCHA can significantly enhance the security and robustness of traditional audio CAPTCHA systems while maintaining similar usability.