ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks (1810.07248v3)

Abstract: Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged. In this paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark) which can be adapted for any desired transform space. The framework is composed of two Fully Convolutional Neural Networks with the residual structure for embedding and extraction. The whole deep network is trained end-to-end to conduct a blind secure watermarking. The framework is customizable for the level of robustness vs. imperceptibility. It is also adjustable for the trade-off between capacity and robustness. The proposed framework simulates various attacks as a differentiable network layer to facilitate end-to-end training. For JPEG attack, a differentiable approximation is utilized, which drastically improves the watermarking robustness to this attack. Another important characteristic of the proposed framework, which leads to improved security and robustness, is its capability to diffuse watermark information among a relatively wide area of the image. Comparative results versus recent state-of-the-art researches highlight the superiority of the proposed framework in terms of imperceptibility and robustness.

• The paper presents a novel approach using fully convolutional networks to embed and extract resilient watermarks.
• It incorporates a differentiable attack simulation layer during training to enhance robustness against common image distortions.
• Diffusion watermarking disperses data across images, improving security and imperceptibility over traditional techniques.

Overview of ReDMark: A Deep Learning-Based Watermarking Framework

The paper "ReDMark: Framework for Residual Diffusion Watermarking based on Deep Networks" presents an end-to-end solution for digital watermarking using deep neural networks. The authors propose a method leveraging Fully Convolutional Networks (FCNs) that aims to enhance watermarking by addressing the fundamental challenges of robustness (resilience to attacks) and imperceptibility (minimal visual distortion). The main contribution of the paper is the development of a framework, ReDMark, which diffuses watermark data across images to increase the security and reliability of the watermarking process, all within a deep learning context.

Technical Contributions

1. Architecture Design: The framework consists of two FCNs tasked with embedding and extracting watermark data. The architecture includes a series of convolutional layers organized with residual connections, specifically designed for end-to-end training. This layout allows for more effective learning and integration of the watermarking process into the image structure.
2. Embedding and Extraction: Watermark data is embedded and extracted using deep networks in a transform space. The networks are trained to perform these tasks autonomously, eschewing traditional fixed algorithmic approaches for dynamic, learned patterns. The embedding network adds a residual watermark to the image, informed by learned patterns that can withstand certain alterations.
3. Differentiable Attack Simulation: An innovative aspect of the ReDMark framework is its use of a differentiable attack layer that simulates potential real-world attacks during the training phase. These attacks include Gaussian noise, JPEG compression, cropping, and others, but skilled handling of JPEG attack simulation through a differentiable approximation of the quantization step distinguishes this work. The approach improves network robustness by training directly against these simulated attacks.
4. Transform Layer Flexibility: The paper expounds on the framework's capability to operate within any linear transform domain. While here demonstrated with a fixed DCT transform, the system potentially allows for adjustable transforms that could be optimized during training, offering flexibility and broader applicability.
5. Diffusion Watermarking: One of the fundamental innovations is the concept of diffusion watermarking, where watermark data is dispersed over a larger image area than traditionally done. This method enhances robustness against block-specific attacks and malicious alterations by ensuring redundant data placements.

Numerical Results and Implications

The results demonstrate superior performance of ReDMark in terms of watermark imperceptibility and robustness when compared with state-of-the-art techniques like non-blind CNN-based auto-encoders and unified CNN-GAN systems. Specifically, the paper reports enhancements in terms of PSNR and SSIM metrics under several attack conditions, proving the practical effectiveness of this approach.

For future developments, the application of such a framework implies potential enhancements in secure content distribution and authenticity verification in digital communication systems. Furthermore, the adaptability of the transform space and expandable attack simulation layers could stimulate research into evolving security threats in watermarking processes.

Conclusion

ReDMark exemplifies an advanced integration of deep learning methodologies in the field of digital watermarking, charting a path not only towards increased security and resilience of digital content protection but also underscoring the adaptable, learning-based approaches to be leveraged in facing emerging threats. The proposed framework enriches the landscape of digital watermarking research with its methodical application of deep learning principles and lays down a promising foundation for future exploration into more resilient and imperceptible watermarking techniques.