Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems

Steffen Egger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., “!d10t”) or as a writing style (“1337” in “leet speak”), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual perturbations demonstrate. We investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentencelevel tasks, showing that both neural and nonneural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82%. We then explore three shielding methods—visual character embeddings, adversarial training, and rule-based recovery—which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.
Original languageEnglish
Title of host publicationProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Place of PublicationMinneapolis, Minnesota
PublisherACL
Pages1634–1647
Number of pages14
VolumeN19-1
DOIs
Publication statusPublished - 7 Jun 2019
Event2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics
- Hyatt Regency, 1300 Nicollet Mall, Minneapolis, MN, Minneapolis, United States
Duration: 2 Jun 20197 Jun 2019
https://naacl2019.org/

Conference

Conference2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Abbreviated titleNAACL 2019
CountryUnited States
CityMinneapolis
Period2/06/197/06/19
Internet address

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