The Limits of Speech Systems: Navigating Adversarial and Poisoning Threats with Robust Defenses.

Abstract: The rapid proliferation of voice-controlled devices and speech recognition systems has heightened the need for robust security measures to safeguard their reliability and trustworthiness. These technologies are increasingly targeted by adversarial and data poisoning attacks, which exploit system vulnerabilities to degrade performance or manipulate outputs. This talk examines the evolving threat landscape for speech systems, with a focus on the detection and classification of adversarial attacks to better understand their mechanisms and impacts. We further explore both dirty- and clean-label poisoning strategies, where malicious data is covertly embedded into training sets, undermining model integrity. Finally, we present and evaluate a range of defense strategies designed to counteract such threats, strengthening the resilience of speech recognition systems against manipulation.