Lture M.Z., S.R., L.P., M.C., M.P., R.S., P.D. and M.M., the statistic results have been completed by M.Z., M.P. and R.S. All authors have read and agreed towards the published version in the manuscript. Funding: This study was founded by the CULS Prague, under Grant IGA PEF CZU (CULS) nr. 2019B0006–Atributy r enalternativn h business modelu v produkci potravin–and Analysis of organic meals buy through the Covid-19 pandemic with utilizing multidimensional statistical strategies, nr. 1170/10/2136 College of Polytechnics in Jihlava. Institutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: This investigation was supported by the CULS Prague, under Grant IGA PEF CZU (CULS) nr. 2019B0006–Atributy r enalternativn h business enterprise modelu v produkci potravin–and Evaluation of organic meals buy through the Covid-19 pandemic with employing multidimensional statistical techniques, nr. 1170/10/2136 College of Polytechnics in Jihlava. Conflicts of Interest: The authors declare no conflict of interest.Agriculture 2021, 11,14 of
applied sciencesArticleUniversal Adversarial Attack through Conditional Sampling for Text ClassificationYu Zhang , Kun Shao , Junan Yang and Hui LiuInstitute of Electronic Countermeasure, National University of Defense Technologies, Hefei 230000, China; [email protected] (Y.Z.); [email protected] (K.S.); [email protected] (H.L.) Correspondence: [email protected] These authors contributed equally to this function.Citation: Zhang, Y.; Shao, K.; Yang, J.; Liu, H. Universal Adversarial Attack by way of Conditional Sampling for Text Classification. Appl. Sci. 2021, 11, 9539. https://doi.org/10.3390/ app11209539 Academic Editor: Luis Javier Garcia Villalba, Rafael T. de Sousa Jr., Robson de Oliveira Albuquerque and Ana Lucila Sandoval Orozco Received: 4 August 2021 Accepted: 12 October 2021 Published: 14 OctoberAbstract: In spite of deep neural networks (DNNs) getting accomplished impressive performance in different domains, it has been 4-Hydroxychalcone site revealed that DNNs are vulnerable in the face of adversarial examples, which are maliciously crafted by adding human-imperceptible perturbations to an original sample to cause the wrong output by the DNNs. Encouraged by a lot of researches on adversarial examples for personal computer vision, there has been expanding interest in designing adversarial attacks for Natural Language Processing (NLP) tasks. Even so, the adversarial attacking for NLP is difficult because text is discrete data in addition to a small perturbation can bring a notable shift for the original input. In this paper, we propose a novel technique, depending on conditional BERT sampling with various requirements, for generating universal adversarial perturbations: input-agnostic of words that can be Combretastatin A-1 Autophagy concatenated to any input to be able to make a specific prediction. Our universal adversarial attack can develop an look closer to natural phrases and yet fool sentiment classifiers when added to benign inputs. Determined by automatic detection metrics and human evaluations, the adversarial attack we created substantially reduces the accuracy with the model on classification tasks, along with the trigger is significantly less very easily distinguished from all-natural text. Experimental outcomes demonstrate that our method crafts much more high-quality adversarial examples as in comparison to baseline approaches. Additional experiments show that our system has higher transferability. Our goal is always to prove that adversarial attacks are much more tough to d.