… [15], many machine learning performance measures are not suitable in the context of security,
since they may provide insufficient estimations or obscure experimental results. In addition…

… rank of the classification layer and the first residual block. For BERT, we plot the gradient rank
of … For SVHN and CIFAR10 datasets, we conduct MI attacks on trained WRN28-4 models. …

… 64], we focus on decision-based black-box attacks against NRMs for the adversarial
ranking attack task. This choice is motivated by the fact that the majority of real-world search …

… a Rank Analysis method, which can be used to estimate the risk of gradient attacks inherent
in … or closed-form-recursive attack is used. Experimental results demonstrate the utility of the …

… , which is why the ranking score doesn’t … machine learning algorithms. The algorithms B1–7
in the table are those from the competition with the number indicating their original ranking. …

… ranking list which is more significant for retrieval. We argue it can not solve the retrieval
attack … We conclude that our proposed ranking distillation attack is practical, when the model …

… problem studied in classical 209 machine learning with image and video processing …
can be explained 212 with a low-rank background plus a sparse part which is decisive 213 …

… a Rank attack named DCB-Attack that targets the latter process in RPL topologies is proposed.
This mechanism uses a trust threshold based on the ranks … , lightweight machine learning-…

… The main contribution of this work is to improve tensornetwork machine learning by
introducing more flexibility concerning the number of parameters and substantially reducing …

… -rank reconstructions and/or transformation of the attacked data has a significant alleviating
effect on the attack, … in adversarial machine learning show that machine learning algorithms …