CRII: CIF: Automated and Robust Image Watermarking: A Deep Learning Approach

CRII：CIF：自动且鲁棒的图像水印：一种深度学习方法

• 批准号: 2104267
• 负责人: Xin Zhong
• 金额: $ 17.5万
• 依托单位: University of Nebraska at Omaha
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104267&HistoricalAwards=false
• 关键词: CRII; CIF; Automated; Robust; Image

Digital image watermarking refers to the process of covertly embedding information into a cover-image and extracting it from it the marked-image; it is used in various application areas ranging from covert communication to authentication to security. Although many handcrafted watermarking schemes are available, these traditional methods run into difficulties due to the limited scope inherent to manual design. To implement image watermarking which adapts to the demands of increasingly diverse application scenarios, this project aims to develop novel schemes based on ideas from deep learning (DL). Two major problems will be addressed, namely (i) minimizing the requirement of domain knowledge, and (ii) achieving robustness without prior knowledge. Outcomes of this project will contribute to a new generation of robust and intelligent watermarking tools that can support cutting-edge applications such as camera scans and secured Internet-of-Things device on-boarding. The integration of the proposed research activities into university curriculum development and other educational programs will contribute to STEM education at various levels. This project seeks to advance the state-of-the-art in DL—based image watermarking through the development of image watermarking schemes that achieve a robust generalization of watermarking rules without requiring information about labeling, the original images, or distortions. The research agenda is structured around two complementary research activities: (i) DL—based automated image watermarking with similarity measures of distance functions, discriminator classifiers, or metric learning; and (ii) DL—based robust image watermarking that explores invariant image latent spaces and automatic rectification. The schemes to be developed will be tested on different applications to confirm their practicality. These research activities are expected to advance our understanding of watermarking on a number of fronts, namely (i) how to design deep learning components (such as architectures and layers) and novel algorithms (through similarity measures) to fully generalize image features and functions for image watermarking processes; (ii) how to design DL components to achieve robustness to different types of distortions in image watermarking, without requiring prior knowledge or adversarial examples; and (iii) how these designs can enable various novel watermarking application scenarios and use cases.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.

数字图像水印是指将信息隐蔽地嵌入到封面图像中并从中提取标记图像的过程；它被用于从隐蔽通信到身份验证到安全的各种应用领域。由于手动设计固有的范围有限，这些传统方法遇到了困难，为了实现适应日益多样化的应用场景的需求，该项目旨在开发基于深度学习（DL）思想的新颖方案。问题将解决以下问题，即（i）最大限度地减少对领域知识的要求，以及（ii）在没有先验知识的情况下实现鲁棒性，该项目的成果将有助于开发新一代强大且智能的水印工具，这些工具可以支持尖端应用程序，例如。摄像头扫描和安全物联网设备接入将拟议的研究活动整合到大学课程开发和其他教育计划中，该项目旨在促进各个层面的 STEM 教育。基于深度学习的图像艺术通过开发图像水印方案来实现水印规则的稳健泛化，而不需要有关标签、原始图像或失真的信息。研究议程围绕两项互补的研究活动构建：（i）基于深度学习的自动图像水印。距离函数、判别器分类器或度量学习的相似性度量；以及（ii）基于深度学习的鲁棒图像水印，探索不变的图像潜在空间和自动校正。这些研究活动预计将增进我们对水印在多个方面的理解，即（i）如何设计深度学习组件（例如架构和层）和新颖的算法（通过相似性度量）来实现完全概括图像水印过程的图像特征和功能；（ii）如何设计深度学习组件以实现对图像水印中不同类型的失真的鲁棒性，而无需先验知识或对抗性示例；（iii）这些设计如何能够实现新颖的各种效果；水印应用场景和用例。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

FOD-A: A Dataset for Foreign Object Debris in Airports

FOD-A：机场异物碎片数据集

• DOI: 10.1007/978-3-030-71454-3_7
• 发表时间: 2021-10-06
• 期刊: ArXiv
• 作者: Travis J. E. Munyer;Pei;Chenyu Huang;Xin Zhong
• 通讯作者: Xin Zhong

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

一种基于深度神经网络的自动化鲁棒图像水印方案

• DOI: 10.1109/tmm.2020.3006415
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Multimedia
• 影响因子: 7.3
• 作者: Zhong, Xin;Huang, Pei;Mastorakis, Spyridon;Shih, Frank Y.
• 通讯作者: Shih, Frank Y.

A Deep Learning-based Audio-in-Image Watermarking Scheme

一种基于深度学习的图像音频水印方案

• DOI: 10.1109/vcip53242.2021.9675375
• 发表时间: 2021-10-06
• 期刊: 2021 International Conference on Visual Communications and Image Processing (VCIP)
• 作者: A. Das;Xin Zhong
• 通讯作者: Xin Zhong

DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding

DLWIoT：基于深度学习的水印，用于授权物联网接入

• DOI: 10.1109/ccnc49032.2021.9369515
• 发表时间: 2021-01
• 期刊: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC
• 作者: Mastorakis S;Zhong X;Huang PC;Tourani R
• 通讯作者: Tourani R