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Generating Adversarial Inputs Using A Black-box Differential Technique
[article]
2020
arXiv
pre-print
Neural Networks (NNs) are known to be vulnerable to adversarial attacks. A malicious agent initiates these attacks by perturbing an input into another one such that the two inputs are classified differently by the NN. In this paper, we consider a special class of adversarial examples, which can exhibit not only the weakness of NN models - as do for the typical adversarial examples - but also the different behavior between two NN models. We call them difference-inducing adversarial examples or
arXiv:2007.05315v1
fatcat:vajwajbasjemvd3aydz5iaup4e