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

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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.