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、法律司法、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改进深度自然语言处理框架的对抗性鲁棒性的技术，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。这个话题是一个新的、令人兴奋的领域，可以为多个学科做出贡献，包括对抗性机器学习、自然语言处理和软件测试；该项目将支持几名研究生在这些领域接受先进的跨学科培训。该奖项将对抗性文本示例定义为深度NLP系统的输入，恶意设计用于欺骗预测性深度NLP模型进行错误预测，同时满足面向语言的约束。目的是研究深度自然语言处理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综合基准，用于跨多个NLP公式生成对抗性文本输入。一个名为TextAttack的库将帮助研究人员评估他们的NLP模型的健壮性，并为攻击设计者提供一个统一的框架，以对当前最先进的攻击进行基准测试。第二项任务是研究深度NLP解释策略的鲁棒性，并设计广义对抗性文本来揭示NLP解释中的漏洞。第三个任务调整软件测试的工作，以创建标准，定义何时生成了一组足够的对抗性文本示例。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，并开发技术来实现深度NLP的鲁棒预测和鲁棒解释。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.