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Universal adversarial perturbations for multiple classification tasks with quantum classifiers. (arXiv:2306.11974v1 [quant-ph])
cs.CR updates on arXiv.org arxiv.org
Quantum adversarial machine learning is an emerging field that studies the
vulnerability of quantum learning systems against adversarial perturbations and
develops possible defense strategies. Quantum universal adversarial
perturbations are small perturbations, which can make different input samples
into adversarial examples that may deceive a given quantum classifier. This is
a field that was rarely looked into but worthwhile investigating because
universal perturbations might simplify malicious attacks to a large extent,
causing unexpected devastation to quantum machine learning models. In this …
adversarial classification defense defense strategies emerging input machine machine learning may quantum studies systems vulnerability