ERI: Towards Robust and Secure Intelligent 3D Sensing Systems

ERI:迈向稳健、安全的智能 3D 传感系统

基本信息

  • 批准号:
    2347426
  • 负责人:
  • 金额:
    $ 19.99万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-06-01 至 2026-05-31
  • 项目状态:
    未结题

项目摘要

The advancement of 3D sensing technology, integral to modern applications like autonomous driving, identity verification, and industrial design, represents a significant leap forward in our ability to interact with and understand the environment around us. By making depth perception more accessible and cost-effective, these technologies open new avenues for innovation and practical application, enhancing national prosperity and welfare. However, as these technologies become more embedded in our daily lives, the security of the systems they enable has emerged as a critical concern. The quest for developing robust and secure intelligent 3D sensing systems becomes crucial for the reliable functioning of cyber-physical systems and infrastructure. This project seeks to address these challenges by developing a secure framework for intelligent 3D sensing, focusing on applications such as autonomous vehicles where safety and reliability are paramount. The aim is to safeguard these technologies from potential threats, ensuring they contribute positively to societal advancement and the secure progression of science. This project aims to share its insights and breakthroughs by publishing scholarly articles and releasing open-source software, demonstration resources, and datasets to the broader community. Research training opportunities will be provided to both undergraduate and graduate students, with proactive efforts to attract candidates from underrepresented groups. Through these efforts, the project will advance our understanding and capabilities in 3D sensing technologies and prepare a new generation of students and engineers equipped with the knowledge and tools to tackle future challenges in this rapidly evolving area.This project addresses the emerging security concerns associated with intelligent 3D sensing systems structured around three interlinked research tasks. The first task delves into understanding the unique nature of out-of-distribution (OOD) samples in the 3D deep learning domain. Unlike their 2D counterparts, OOD samples in 3D possess distinct characteristics and distributions that require thorough investigation to mitigate their potential impact on system reliability. The second task focuses on developing a comprehensive framework that enhances robustness across various aspects and stages of the perception pipeline. This framework aims to protect against physical adversarial threats, ensuring the security of 3D sensing applications in critical environments. The final task, the evaluation phase, involves the creation of simulation tools and testbed platforms. These tools will validate the findings and methodologies developed in the first two tasks through extensive testing with existing and newly collected datasets and implementation in real-world scenarios. This comprehensive approach ensures that the project not only addresses theoretical challenges but also validates its findings in practical applications, paving the way for deploying secure, intelligent 3D sensing systems in many settings. By integrating rigorous research with practical validation, this project aims to significantly contribute to intelligent 3D sensing, enhancing its reliability and security.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.
3D 传感技术的进步是自动驾驶、身份验证和工业设计等现代应用不可或缺的一部分,代表着我们与周围环境互动和理解周围环境的能力的重大飞跃。通过使深度感知变得更容易获得和更具成本效益,这些技术为创新和实际应用开辟了新的途径,从而促进国家繁荣和福祉。然而,随着这些技术越来越融入我们的日常生活,它们所支持的系统的安全性已成为一个关键问题。寻求开发强大且安全的智能 3D 传感系统对于网络物理系统和基础设施的可靠运行至关重要。该项目旨在通过开发智能 3D 传感的安全框架来应对这些挑战,重点关注安全性和可靠性至关重要的自动驾驶汽车等应用。目的是保护这些技术免受潜在威胁,确保它们为社会进步和科学的安全进步做出积极贡献。该项目旨在通过发表学术文章并向更广泛的社区发布开源软件、演示资源和数据集来分享其见解和突破。将向本科生和研究生提供研究培训机会,并积极努力吸引来自代表性不足群体的候选人。通过这些努力,该项目将增进我们对 3D 传感技术的理解和能力,并为新一代学生和工程师做好准备,配备知识和工具来应对这个快速发展的领域的未来挑战。该项目解决了与智能 3D 传感系统围绕三个相互关联的研究任务构建。第一项任务深入了解 3D 深度学习领域中分布外 (OOD) 样本的独特性质。与 2D 样本不同,3D 中的 OOD 样本具有独特的特征和分布,需要进行彻底的调查以减轻其对系统可靠性的潜在影响。第二项任务侧重于开发一个全面的框架,以增强感知管道各个方面和阶段的稳健性。该框架旨在防范物理对抗威胁,确保关键环境中 3D 传感应用的安全。最后的任务,即评估阶段,涉及创建模拟工具和测试平台。这些工具将通过对现有和新收集的数据集进行广泛测试以及在现实场景中的实施来验证前两项任务中开发的发现和方法。这种全面的方法确保该项目不仅能够解决理论挑战,而且还能在实际应用中验证其发现,为在许多环境中部署安全、智能的 3D 传感系统铺平道路。通过将严谨的研究与实际验证相结合,该项目旨在为智能 3D 传感做出重大贡献,提高其可靠性和安全性。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。

项目成果

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Kaichen Yang其他文献

CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples
CloudLeak:大规模深度学习模型通过对抗性示例进行窃取
Graph Neural Network based Hardware Trojan Detection at Intermediate Representative for SoC Platforms
SoC 平台中级代表基于图神经网络的硬件木马检测
Robust Adversarial Objects against Deep Learning Models
针对深度学习模型的鲁棒对抗对象
  • DOI:
    10.1609/aaai.v34i01.5443
  • 发表时间:
    2020-04-03
  • 期刊:
  • 影响因子:
    1.4
  • 作者:
    Tzungyu Tsai;Kaichen Yang;Tsung;Yier Jin
  • 通讯作者:
    Yier Jin
Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge
硬件 Phi-1.5B:编码硬件领域特定知识的大型语言模型
Robust Roadside Physical Adversarial Attack Against Deep Learning in Lidar Perception Modules
针对激光雷达感知模块中深度学习的强大路边物理对抗攻击

Kaichen Yang的其他文献

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{{ truncateString('Kaichen Yang', 18)}}的其他基金

Travel: Travel support for SmartSP 2023
旅行:SmartSP 2023 的旅行支持
  • 批准号:
    2330018
  • 财政年份:
    2023
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Standard Grant

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