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Creators/Authors contains: "Sen, Ayon"

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  1. Adversarial attacks on image classification aim to make visually imperceptible changes to induce misclassification. Popular computational definitions of imperceptibility are largely based on mathematical convenience such as pixel p-norms. We perform a behavioral study that allows us to quantitatively demonstrate the mismatch between human perception and popular imperceptibility measures such as pixel p-norms, earth mover’s distance, structural similarity index, and deep net embedding. Our results call for a reassessment of current adversarial attack formulation. 
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