We introduce a novel adversarial attack methodology inspired by the K-anonymity principles, that generates adversarial examples that are not only misclassified.
INTRODUCING K-ANONYMITY PRINCIPLES TO ADVERSARIAL ATTACKS FOR. PRIVACY PROTECTION IN IMAGE CLASSIFICATION PROBLEMS. Vasileios Mygdalis, Anastasios Tefas and ...
Oct 28, 2021 · Adversarial attacks in classification tasks aim to generate the minimum amount of perturbation required to be added to the inputs, in order to ...
Adversarial attacks are often obtained by making subtle perturbations to normal images, which are mostly imperceptible to humans, but can seriously confuse the ...
Introducing K-Anonymity Principles to Adversarial Attacks for Privacy Protection in Image Classification Problems. October 2021. DOI:10.1109/MLSP52302 ...
Introducing K-anonymity principles to adversarial attacks for privacy protection in image classification problems ... New Use Cases Demo Videos Now Live! #61.
The proposed K-A introduces novel optimization criteria to standard adversarial attack methodologies, inspired by the K-Anonymity principles. Its generated ...
This work introduces a novel adversarial attack methodology inspired by the K-anonymity principles, that generates adversarial examples that are not only ...
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Feb 6, 2020 · Abstract. A novel adversarial attack methodology for fooling deep neural network classifiers in image classification.
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