NSF Convergence Accelerator Track F: Online Deception Awareness and Resilience Training (DART)

NSF 融合加速器轨道 F:在线欺骗意识和弹性培训 (DART)

基本信息

  • 批准号:
    2230494
  • 负责人:
  • 金额:
    $ 500万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Cooperative Agreement
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-15 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

As one of the most vexing problems of the century, we are witnessing the escalating speed, scale, and level of sophistication of online deception (spear phishing and catfishing scams, personal information hunting schemes, fake content, impersonation, and disinformation on social media) that have severe consequences (ransomware attack, financial loss, and breach of private information). The most vulnerable demographic is older adults, who are disproportionately targeted for online exploitation, manipulation, and fraud resulting in significant financial loss and emotional distress. Deception Awareness and Resilience Training (DART) aims to equip older adults with the tools they need to recognize various forms of online deception and help others in their social circle avoid or mitigate harm. Designed by experts in education, psychology, communication, cybersecurity, and media studies, the DART curriculum contains high-quality and timely synthetic contents and real-world scenarios. The DART project team includes experts in psychology, communications and media, economics, cybersecurity, computer science, game design, synthetic media, and aging studies. DART deliverables will be developed by a professional development team and tested with older adults.The DART system consists of two complementary components: (1) DART Learn: a web-based structured, dynamic, and self-paced learning program on online deceptions. (2) DART Practice: an interactive social media simulation that provides a safe and realistic platform for the users to practice what they learned about online deception. (3) DART Play: a set of simple, fun mobile Games on mobile platforms (iOS and Android) designed to familiarize older adults with common deceptions.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.
作为世纪最令人烦恼的问题之一,我们正在目睹网络欺诈(鱼叉式网络钓鱼和鲶鱼式网络钓鱼诈骗、个人信息狩猎计划、虚假内容、冒充和社交媒体上的虚假信息)的速度、规模和复杂程度不断升级,并产生严重后果(勒索软件攻击、财务损失和私人信息泄露)。最脆弱的人群是老年人,他们不成比例地成为在线剥削,操纵和欺诈的目标,导致重大的经济损失和情绪困扰。欺骗意识和复原力培训(DART)旨在为老年人提供识别各种形式的在线欺骗所需的工具,并帮助社交圈中的其他人避免或减轻伤害。由教育,心理学,通信,网络安全和媒体研究专家设计,DART课程包含高质量和及时的合成内容和真实世界的场景。DART项目团队包括心理学、通信和媒体、经济学、网络安全、计算机科学、游戏设计、合成媒体和老龄化研究方面的专家。DART系统由两个互补的部分组成:(1)DART Learn:一个基于网络的结构化、动态和自定进度的在线欺骗学习计划。(2)DART实践:这是一个互动的社交媒体模拟,为用户提供了一个安全和现实的平台,以实践他们对在线欺骗的了解。(3)DART游戏:一套简单,有趣的手机游戏在移动的平台(iOS和Android),旨在熟悉老年人与常见的欺骗。这个奖项反映了NSF的法定使命,并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

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Siwei Lyu其他文献

Countering JPEG anti-forensics based on noise level estimation
基于噪声水平估计的 JPEG 反取证对抗
  • DOI:
    10.1007/s11432-016-0426-1
  • 发表时间:
    2017-08
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hui Zeng;Xiangui Kang;Jingjing Yu;Siwei Lyu
  • 通讯作者:
    Siwei Lyu
Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
基于结构感知超图的在线变形目标跟踪
  • DOI:
    10.1109/tip.2016.2570556
  • 发表时间:
    2016-08
  • 期刊:
  • 影响因子:
    10.6
  • 作者:
    Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
  • 通讯作者:
    Siwei Lyu
Deep Constrained Low-Rank Subspace Learning for Multi-View Semi-Supervised Classification
用于多视图半监督分类的深度约束低秩子空间学习
  • DOI:
    10.1109/lsp.2019.2923857
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhe Xue;Junping Du;Dawei Du;Guorong Li;Qingming Huang;Siwei Lyu
  • 通讯作者:
    Siwei Lyu
Vertebral artery course variation leading to an insufficient proximal anchoring area for thoracic endovascular aortic repair.
椎动脉走行变化导致胸主动脉腔内修复的近端锚固区域不足。
  • DOI:
    10.1177/17085381221140319
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    1.1
  • 作者:
    Zuanbiao Yu;Siwei Lyu;Dehai Lang;Di Wang;Songjie Hu;Xiaoliang Yin;Yunpeng Ding;Chunbo Xu;Chen Lin;Jiangnan Hu
  • 通讯作者:
    Jiangnan Hu
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction
将非最大似然学习目标与最小 KL 收缩统一起来

Siwei Lyu的其他文献

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{{ truncateString('Siwei Lyu', 18)}}的其他基金

SaTC: CORE: Small: Combating AI Synthesized Media Beyond Detection
SaTC:核心:小型:对抗无法检测的人工智能合成媒体
  • 批准号:
    2153112
  • 财政年份:
    2022
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator Track F: A Disinformation Range to Improve User Awareness and Resilience to Online Disinformation
NSF 融合加速器轨道 F:提高用户对在线虚假信息的认识和抵御能力的虚假信息范围
  • 批准号:
    2137871
  • 财政年份:
    2021
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
RI: Small: A Study of New Aggregate Losses for Machine Learning
RI:小:机器学习新总损失的研究
  • 批准号:
    2008532
  • 财政年份:
    2020
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
RI: Small: A Study of New Aggregate Losses for Machine Learning
RI:小:机器学习新总损失的研究
  • 批准号:
    2103450
  • 财政年份:
    2020
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
NRI: Collaborative Research: A Dynamic Bayesian Approach to Real Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks (Continuation)
NRI:协作研究:抓取采集和其他接触任务中实时估计和过滤的动态贝叶斯方法(续)
  • 批准号:
    1537257
  • 财政年份:
    2015
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
Blind Noise Estimation Using Signal Statistics in Random Band-Pass Domains
使用随机带通域中的信号统计进行盲噪声估计
  • 批准号:
    1319800
  • 财政年份:
    2013
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
NRI-Small: Collaborative Research: A Dynamic Bayesian Approach to Real-Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks
NRI-Small:协作研究:在抓取采集和其他接触任务中进行实时估计和过滤的动态贝叶斯方法
  • 批准号:
    1208463
  • 财政年份:
    2012
  • 资助金额:
    $ 500万
  • 项目类别:
    Standard Grant
CAREER: A New Statistical Framework for Natural Images with Applications in Vision
职业:一种新的自然图像统计框架及其在视觉中的应用
  • 批准号:
    0953373
  • 财政年份:
    2010
  • 资助金额:
    $ 500万
  • 项目类别:
    Continuing Grant

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