SaTC: CORE: Small: Generalizing Adversarial Examples in Natural Language

基于深度学习的自然语言处理（deep NLP）在许多安全关键领域发挥着至关重要的作用，包括推进医疗保健、法律的正义、电子商务和社交媒体平台的信息理解和分析。 因此，了解深度NLP系统对旨在降低其准确性和安全性的对抗性攻击的鲁棒性至关重要。为了对抗这些攻击，该项目引入了自动评估和提高深度NLP框架的对抗鲁棒性的技术，以及可以作为有用的社区基准和研究资源的工具和数据集。这个主题是一个新的和令人兴奋的领域，可以为多个学科做出贡献，包括对抗机器学习，自然语言处理和软件测试;该项目将支持几名研究生接受先进的，该奖项将对抗性文本示例定义为深度NLP系统的输入，这些系统被恶意设计为欺骗预测性深度NLP模型进行错误的预测，同时满足面向语言的约束。 我们的目标是研究深度NLP和对抗鲁棒性在三个相关任务中的相互作用。第一个任务是建立一个全面的基准，用于在多个NLP公式中生成对抗性文本输入。一个库，TextAttack，将帮助研究人员衡量他们的NLP模型的鲁棒性，并为攻击设计者提供一个统一的框架，以基准对当前最先进的攻击。第二个任务调查的鲁棒性解释策略在深NLP和设计广义的对抗性文本，以揭示在NLP解释的漏洞。第三个任务改编了软件测试的工作，以创建定义何时生成足够的对抗性文本示例集的标准。总之，该项目研究如何评估最先进的NLP系统对对手的鲁棒性，并开发技术，以实现深度NLP的鲁棒预测和鲁棒解释。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Deep learning-based natural language processing (deep NLP) plays a crucial role in many security-critical domains, including advancing information understanding and analysis for healthcare, legal justice, e-commerce, and social media platforms. Consequently, it is essential to understand the robustness of deep NLP systems to adversarial attacks aimed at reducing their accuracy and security. To combat these attacks, this project introduces techniques to automatically evaluate and improve the adversarial robustness of deep NLP frameworks, as well as tools and datasets that can serve as useful community benchmarks and research resources. This topic is a new and exciting area that can contribute to multiple disciplines, including adversarial machine learning, natural language processing, and software testing; the project will support several graduate students in receiving advanced, interdisciplinary training in these areas.This award defines adversarial text examples as inputs to a deep NLP system that are maliciously designed to fool a predictive deep NLP model towards wrong predictions while simultaneously satisfying language-oriented constraints. The goal is to investigate the interplay between deep NLP and adversarial robustness in three dependent tasks. The first task is to build a comprehensive benchmark for generating adversarial text inputs across multiple NLP formulations. A library, TextAttack, will help researchers gauge their NLP models' robustness and provide a unified framework for attack designers to benchmark their attacks against the current state-of-the-art. The second task investigates the robustness of interpretation strategies in deep NLP and designs generalized adversarial text to reveal vulnerabilities in NLP interpretations. The third task adapts work from software testing to create criteria that define when an adequate set of adversarial text examples has been generated. In summary, this project studies how to evaluate the robustness of state-of-the-art NLP systems against an adversary and develop techniques to achieve both robust predictions and robust interpretations in deep NLP.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.