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Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands -audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks.

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Waste materials from natural sources are important resources for extraction and recovery of valuable compounds. Transformation of these waste materials into valuable materials requires specific techniques and approaches. Hydroxyapatite (HAp) is a biomaterial that can be extracted from natural wastes. HAp has been widely used in biomedical applications owing to its excellent bioactivity, high biocompatibility, and excellent osteoconduction characteristics. Thus, HAp is gaining prominence for applications as orthopaedic implants and dental materials. This review summarizes some of the recent methods for extraction of HAp from natural sources including mammalian, aquatic or marine sources, shell sources, plants and algae, and from mineral sources. The extraction methods used to obtain hydroxyapatite are also described. The effect of extraction process and natural waste source on the critical properties of the HAp such as Ca/P ratio, crystallinity and phase assemblage, particle sizes, and morphology are discussed herein.