EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Improving Human Discernment of Audio Deepfakes via Multi-level Information Augmentation

这个项目通过增加技术和社会语言方面的信息来提高听众对音频深伪的辨别能力。该项目为跨越社会语言学、以人为中心的分析和数据科学的协作研究建立了一条创新的道路，并为未来从人类行为角度了解到的跨学科广泛相关的深度虚假分析奠定了基础。该项目将通过产生洞察力来应对错误信息的社会挑战，从而提高听众--尤其是大学生--评估在线信息的真实性和真实性的能力，他们的生活受到科技的不可磨灭的影响。该项目更广泛的意义是通过产生洞察力来解决错误信息的社会挑战，这些洞察力可以帮助听众就如何评估他们在网上遇到的信息的真实性和真实性做出决定。该项目提高了对深度假如何参与传播错误信息和追踪语言技术如何被适应社会危害和/或以不道德的方式使用的理解和建模。这项拟议的工作将通过利用综合跨学科知识的信息增强来提高听众对音频深伪的识别能力，并将数据增强作为深度假检测的重要工具。该项目的目标是：(1)研究和评估听者对不同语言复杂程度的音频深伪的感知；(2)研究和评估培训课程的有效性，这些培训增加了听者的社会语言感知能力，提高了他们识别深度伪音频内容的能力；(3)通过多层次的时间和语言签名，通过训练和语言标记来增强音频深伪识别；(4)评估增强签名信息对听者对音频深伪感知的影响；(5)创建社会科学和数据科学学生参与的开放获取在线模块和材料，以提高听众对更广泛公共规模的音频提示的识别能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project increases listeners’ discernment of audio deepfakes through augmentation of information, both technological and sociolinguistic. This project establishes an innovative pathway for collaborative research across sociolinguistics, human centered analytics, and data science and lays the groundwork for future analyses of deepfakes that are broadly relevant across disciplines, informed by human behavioral perspectives. The project will address the societal challenge of misinformation by generating insights that can increase the ability of listeners – particularly college students, whose lives are indelibly shaped by technology – to evaluate the veracity and authenticity of information online. The project's broader significance is to address the societal challenge of misinformation by generating insights that can help empower listeners to make decisions about how to evaluate the veracity and authenticity of information they encounter online. The project improves understanding and modeling of how deepfakes are involved in spreading misinformation and tracking how language technology is adapted for social harm and/or used in unethical ways. The proposed work will increase listeners’ discernment of audio deepfakes through augmentation of information that draws upon integrated interdisciplinary knowledge and advances data augmentation as an important tool for deepfake detection. The objectives of the project are to: (1) Study and evaluate listener perceptions of audio deepfakes that have been created with varying degrees of linguistic complexity; (2) Study and evaluate the efficacy of training sessions that increase listeners’ sociolinguistic perceptual ability and improve their ability to discern deepfake audio content; (3) Augment the audio deepfake discernment via multi-level temporal and linguistic signatures, informed by training and linguistic labeling; (4) Evaluate the impact of augmented signature information on listener perceptions of audio deepfakes; (5) Create open-access online modules and materials with social science and data science student involvement to improve listeners’ discernment of audio cues on a wider public scale.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.