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The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models. (arXiv:2303.08500v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
Protecting personal data against the exploitation of machine learning models
is of paramount importance. Recently, availability attacks have shown great
promise to provide an extra layer of protection against the unauthorized use of
data to train neural networks. These methods aim to add imperceptible noise to
clean data so that the neural networks cannot extract meaningful patterns from
the protected data, claiming that they can make personal data "unexploitable."
In this paper, we provide a strong countermeasure against such approaches, …
aim attacks availability data diffusion models exploitation extract great machine machine learning machine learning models meaningful networks neural networks noise patterns personal personal data protecting protection train