A deep learning model processes speech from a noisy environment. If an adversarial attack subtly alters the input audio to introduce inaudible high-frequency components, which consequence follows?

💡 Explanation

The model misclassifies specific words because adversarial attacks exploit vulnerabilities in the model's encoding process by adding carefully crafted noise. These manipulations, rather than improving general accuracy, target the model's decision boundaries, therefore the result is a misclassification.

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