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    Researchers explored vulnerabilities in speech translation (ST) systems using imperceptible audio manipulations. Attacks, including adapted automatic speech recognition (ASR) techniques and novel music generation, successfully compromised ST models, revealing systemic weaknesses.

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    Area of Science:

    • Natural Language Processing
    • Speech Technology
    • Cybersecurity

    Background:

    • Speech translation (ST) systems are increasingly common, but their security vulnerabilities are not well understood.
    • Robust and reliable communication relies on secure ST systems.
    • Limited research exists on compromising ST systems via audio manipulation.

    Purpose of the Study:

    • To investigate methods for compromising speech translation systems using imperceptible audio manipulations.
    • To adapt existing automatic speech recognition (ASR) attack techniques for ST.
    • To develop novel methods, such as music generation, for covert ST attacks.

    Main Methods:

    • Adapting perturbation-based techniques from automatic speech recognition (ASR) attacks to speech translation (ST).
    • Developing a novel music generation method to guide targeted translations.
    • Conducting over-the-air attacks in real-world scenarios.

    Main Results:

    • Carefully crafted audio perturbations can cause ST models to generate targeted, harmful outputs.
    • Adversarial music can covertly compromise ST systems by exploiting natural imperceptibility.
    • Attacks demonstrated effectiveness across multiple languages and ST models, indicating systemic vulnerabilities.

    Conclusions:

    • Current speech translation architectures exhibit systemic vulnerabilities to audio manipulation attacks.
    • Findings highlight broader challenges in the robustness and interpretability of neural speech systems.
    • Imperceptible audio manipulations pose a significant threat to the security and reliability of ST systems.