Lture M.Z., S.R., L.P., M.C., M.P., R.S., P.D. and M.M., the statistic benefits have been performed by M.Z., M.P. and R.S. All authors have read and agreed for the published version of your manuscript. Funding: This analysis was founded by the CULS Prague, below Grant IGA PEF CZU (CULS) nr. 2019B0006–Atributy r enalternativn h business modelu v produkci potravin–and Analysis of organic meals purchase throughout the Covid-19 pandemic with using multidimensional statistical approaches, nr. 1170/10/2136 College of Polytechnics in Jihlava. Institutional Critique Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: This investigation was supported by the CULS Prague, beneath Grant IGA PEF CZU (CULS) nr. 2019B0006–Atributy r enalternativn h small business modelu v produkci potravin–and Analysis of organic meals buy during the Covid-19 pandemic with utilizing multidimensional statistical strategies, 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 by way of 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 operate.Citation: Zhang, Y.; Shao, K.; Yang, J.; Liu, H. Universal Adversarial Attack by means 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 Chlorobutanol Epigenetics Accepted: 12 October 2021 Published: 14 OctoberAbstract: Regardless of deep neural networks (DNNs) possessing accomplished impressive efficiency in numerous domains, it has been revealed that DNNs are vulnerable within the face of adversarial examples, which are maliciously crafted by adding human-imperceptible perturbations to an original sample to trigger the incorrect output by the DNNs. Encouraged by a lot of researches on adversarial examples for laptop vision, there has been growing interest in designing adversarial attacks for Natural Language Processing (NLP) tasks. However, the adversarial attacking for NLP is challenging because text is discrete information and also a compact perturbation can bring a notable shift towards the original input. Within this paper, we propose a novel system, based on conditional BERT sampling with multiple standards, for creating universal adversarial perturbations: input-agnostic of words that can be concatenated to any input in order to generate a particular prediction. Our universal adversarial attack can produce an look Trilinolein Protocol closer to all-natural phrases and yet fool sentiment classifiers when added to benign inputs. Based on automatic detection metrics and human evaluations, the adversarial attack we developed drastically reduces the accuracy of the model on classification tasks, and the trigger is significantly less simply distinguished from natural text. Experimental final results demonstrate that our process crafts more high-quality adversarial examples as when compared with baseline methods. Additional experiments show that our process has high transferability. Our goal is to prove that adversarial attacks are more hard to d.