Abstract
Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks show low performance in a novel scenario where only a few queries are allowed. To address this issue, we propose gradient aligned attacks (GAA), which use the gradient aligned losses (GAL) we designed on the surrogate model to estimate the accurate gradient to improve the attack performance on the victim model. Specifically, we propose a gradient aligned mechanism to ensure that the derivatives of the loss function with respect to the logit vector have the same weight coefficients between the surrogate and victim models. Using this mechanism, we transform the cross-entropy (CE) loss and margin loss into gradient aligned forms, i.e. the gradient aligned CE or margin losses. These losses not only improve the attack performance of our gradient aligned attacks in the novel scenario but also increase the query ef