# On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks Source: [On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:04:19.938Z Authors: Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi, Osamu Abe Published: 2020-02-18T00:50:26.000Z ## Abstract In general, adversarial perturbations superimposed on inputs are realistic threats for a deep neural network (DNN). In this paper, we propose a practical generation method of such adversarial perturbation to be applied to black-box attacks that demand access to an input-output relationship only. Thus, the attackers generate such perturbation without invoking inner functions and/or accessing the inner states of a DNN. Unlike the earlier studies, the algorithm to generate the perturbation presented in this study requires much fewer query trials. Moreover, to show the effectiveness of the adversarial perturbation extracted, we experiment with a DNN for semantic segmentation. The result shows that the network is easily deceived with the perturbation generated than using uniformly distributed random noise with the same magnitude. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()