Understanding the challenges, position themselves and, ultimately, help them to This paper isĮxpected to serve researchers and practitioners as a reference to help them in Research challenges, defense recommendations, and future work. Speech recognition techniques, then, we investigate the effectiveness ofĪdversarial attacks and defenses against these systems, before outlining While focusing on speech recognitionįor machine-critical applications, in this paper, we first review existing AnĪdversarial attack might entail presenting a model with inaccurate orįabricated samples as it's training data, or introducing maliciously designedĭata to deceive an already trained model. The most common reason ofĪdversarial attacks is to cause a malfunction in a machine learning model. Particular for machine-critical applications. Is a growing threat in the AI and machine learning research community, in Of techniques that attempt to fool machine learning models with deceptive data, Indeed, Adversarial Artificial Intelligence (AI) which refers to a set
While these conversational interfaces haveĪllowed users to give voice commands to carry out strategic and criticalĪctivities, their robustness to adversarial attacks remains uncertain and Speech-based conversational interfaces to interact with various Technologies have naturally allowed the development and deployment of Learning, Natural Language Processing, voice recognition, and speech processing Rescue, military, and emergency management actions.
The success of specific and sensitive operations such as search and recovery, Download a PDF of the paper titled Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey, by Ngoc Dung Huynh and 6 other authors Download PDF Abstract: A Machine-Critical Application is a system that is fundamentally necessary to
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