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Art-Attack: Black-Box Adversarial Attack via Evolutionary Art. (arXiv:2203.04405v1 [cs.CR])
March 10, 2022, 2:20 a.m. | Phoenix Williams, Ke Li
cs.CR updates on arXiv.org arxiv.org
Deep neural networks (DNNs) have achieved state-of-the-art performance in
many tasks but have shown extreme vulnerabilities to attacks generated by
adversarial examples. Many works go with a white-box attack that assumes total
access to the targeted model including its architecture and gradients. A more
realistic assumption is the black-box scenario where an attacker only has
access to the targeted model by querying some input and observing its predicted
class probabilities. Different from most prevalent black-box attacks that make
use of …
More from arxiv.org / cs.CR updates on arXiv.org
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