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Seeking a way of preventing audio models for AI machine learning from being fooled

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Warnings have emerged about the unreliability of the metrics used to detect whether an audio perturbation designed to fool AI models can be perceived by humans. Researchers at the UPV/EHU-University of the Basque Country show that the distortion metrics used to detect intentional perturbations in audio signals are not a reliable measure of human perception, and have proposed a series of improvements. These perturbations, designed to be imperceptible, can be used to cause erroneous predictions in artificial intelligence. Distortion metrics are applied to assess how effective the methods are in generating such attacks.
Artificial intelligence (AI) is increasingly based on machine learning models, trained using large …

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