{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generating-textual-adversarial-examples-for","title":"Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey","arxiv_id":"1901.06796","date":"2019-01-21","proceeding":null,"authors":["Wei Emma Zhang","Quan Z. Sheng","Ahoud Alhazmi","Chenliang Li"],"abstract":"With the development of high computational devices, deep neural networks\n(DNNs), in recent years, have gained significant popularity in many Artificial\nIntelligence (AI) applications. However, previous efforts have shown that DNNs\nwere vulnerable to strategically modified samples, named adversarial examples.\nThese samples are generated with some imperceptible perturbations but can fool\nthe DNNs to give false predictions. Inspired by the popularity of generating\nadversarial examples for image DNNs, research efforts on attacking DNNs for\ntextual applications emerges in recent years. However, existing perturbation\nmethods for images cannotbe directly applied to texts as text data is discrete.\nIn this article, we review research works that address this difference and\ngeneratetextual adversarial examples on DNNs. We collect, select, summarize,\ndiscuss and analyze these works in a comprehensive way andcover all the related\ninformation to make the article self-contained. Finally, drawing on the\nreviewed literature, we provide further discussions and suggestions on this\ntopic.","url_abs":"http://arxiv.org/abs/1901.06796v3","url_pdf":"http://arxiv.org/pdf/1901.06796v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generating-textual-adversarial-examples-for","repo_url":"https://github.com/lihebi/biber","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1901.06796","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}