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.

数字图像水印是指覆盖图像的过程，并将其从标记图像中提取；它用于从秘密通信到身份验证到安全性的各种应用领域。尽管可以使用许多手工制作的水印方案，但由于手动设计固有的范围有限，这些传统方法遇到了困难。为了实施适应日益多样化应用程序方案的需求的图像水印，该项目旨在根据深度学习（DL）的思想开发新的方案。将解决两个主要问题，即（i）最大程度地减少领域知识的要求，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新一代强大而智能的水印工具，这些工具可以支持诸如摄像机扫描和安全的板载设备等尖端应用程序。拟议的研究活动融入大学课程发展和其他教育计划将有助于不同级别的STEM教育。该项目旨在通过开发图像水印方案来推动DL中的最新图像，从而实现对水印规则的强大概括，而无需提供有关标签，原始图像或扭曲的信息。研究议程围绕两个完整的研究活动进行结构：（i）基于距离功能，歧视器分类器或度量学习的基于基于自动图像水印； （ii）DL - 基于稳健的图像水印，探讨了不变的图像潜在空间和自动整流。将要开发的计划将在不同的应用程序上进行测试，以确认其实用性。这些研究活动有望提高我们对许多方面的水印的理解，即（i）如何设计深度学习组件（例如体系结构和层）和新型算法（通过相似性措施），以完全概括图像水印过程的图像特征和功能； （ii）如何设计DL组件以实现图像水印中不同类型的扭曲的鲁棒性，而无需先验知识或对抗性示例； （iii）这些设计如何启用各种新颖的水印应用方案和用例。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准来诚实地通过评估来诚实地支持。

项目成果

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

• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: Travis J. E. Munyer;Pei-Chi Huang;Chenyu Huang;Xin Zhong

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

• DOI: 10.1109/tmm.2020.3006415
• 发表时间: 2021-01-01
• 期刊: IEEE TRANSACTIONS ON MULTIMEDIA
• 影响因子: 7.3
• 作者: Zhong, Xin;Huang, Pei-Chi;Shih, Frank Y.
• 通讯作者: Shih, Frank Y.

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

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