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载体图像中，并从中提取出被标记图像的过程，它被应用于从隐蔽通信到身份认证再到安全等各个领域。虽然许多手工制作的水印方案是可用的，这些传统的方法遇到困难，由于有限的范围内固有的手工设计。为了实现适应日益多样化的应用场景需求的图像水印，该项目旨在基于深度学习（DL）的思想开发新方案。将解决两个主要问题，即（i）最小化领域知识的要求，和（ii）实现鲁棒性没有先验知识。该项目的成果将有助于开发新一代强大而智能的水印工具，这些工具可以支持相机扫描和安全的物联网设备加载等尖端应用。将拟议的研究活动纳入大学课程开发和其他教育计划将有助于各级STEM教育。该项目旨在通过开发图像水印方案来推进基于DL的图像水印的最新技术，该图像水印方案实现了水印规则的鲁棒概括，而不需要关于标签、原始图像或失真的信息。研究议程围绕两个互补的研究活动：（i）基于DL的自动图像水印与距离函数，模糊分类器或度量学习的相似性度量;和（ii）基于DL的鲁棒图像水印，探索不变的图像潜在空间和自动纠正。将在不同的应用程序中测试将开发的计划，以确认其实用性。这些研究活动预计将在多个方面促进我们对水印的理解，即（i）如何设计深度学习组件（如架构和层）和新颖的算法（通过相似性度量）来完全概括图像特征和功能以用于图像水印处理;（ii）如何设计DL分量以实现对图像水印中的不同类型的失真的鲁棒性，而不需要先验知识或对抗性示例;该奖项反映了NSF的法定使命，并通过使用基金会的知识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

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

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

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.