LetterLiGo/Inaudible-Adversarial-Perturbation-Vrifle
[NDSS'24] Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time
This project helps security researchers and system testers evaluate the robustness of Automatic Speech Recognition (ASR) systems like Deep Speech 2. It takes a raw audio file and a target sentence, then generates an 'inaudible' audio perturbation that, when added to the original, makes the ASR system transcribe the target sentence instead of the original spoken content. An adversarial machine learning researcher or a red team security professional would use this to probe vulnerabilities in voice-controlled systems.
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Use this if you need to create 'inaudible' audio attacks to manipulate what an Automatic Speech Recognition (ASR) system transcribes from spoken input.
Not ideal if you are looking for a general-purpose audio manipulation tool or an ASR system itself, as this is specifically for adversarial attacks.
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Python
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Sep 28, 2024
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