arXiv:2402.09062v1 Announce Type: new
Abstract: Digital watermarking enables protection against copyright infringement of images. Although existing methods embed watermarks imperceptibly and demonstrate robustness against attacks, they typically lack resilience against geometric transformations. Therefore, this paper proposes a new watermarking method that is robust against geometric attacks. The proposed method is based on the existing HiDDeN architecture that uses deep learning for watermark encoding and decoding. We add new noise layers to this architecture, namely for a differentiable JPEG estimation, rotation, rescaling, translation, shearing and mirroring. We demonstrate that our method outperforms the state of the art when it comes to geometric robustness. In conclusion, the proposed method can be used to protect images when viewed on consumers’ devices.

Expert Commentary: Robust Geometric Watermarking for Image Protection

This article discusses a new watermarking method that aims to address the challenge of geometric transformations in protecting images against copyright infringement. While existing methods are effective in embedding watermarks imperceptibly and withstanding various attacks, they often fall short when it comes to resilience against geometric transformations.

The proposed method builds upon the HiDDeN architecture, which utilizes deep learning techniques for watermark encoding and decoding. By introducing new noise layers, such as differentiable JPEG estimation, rotation, rescaling, translation, shearing, and mirroring, the authors demonstrate improved robustness against geometric attacks.

The multi-disciplinary nature of this research is noteworthy. It combines concepts from several fields, including image processing, deep learning, and computer vision, to address a specific challenge in the broader field of multimedia information systems.

Watermarking techniques are widely utilized in multimedia systems to protect intellectual property and prevent unauthorized use. Enhancing the protection against geometric transformations is crucial, as it not only contributes to the overall robustness of the watermark but also ensures the integrity of the copyrighted content.

Moreover, this research aligns with advancements in virtual realities, augmented reality, and artificial reality. As these technologies continue to evolve, the need for secure and resilient watermarking methods becomes increasingly important. By protecting images when viewed on consumer devices, the proposed method contributes to ensuring the authenticity and ownership of digital content in virtual and augmented reality environments.

In conclusion, this paper presents a promising approach to robust geometric watermarking for image protection. Through the utilization of deep learning techniques and the incorporation of various geometric transformations, the proposed method demonstrates superior performance compared to existing state-of-the-art methods. This research holds significant potential in safeguarding the integrity of copyrighted images in multimedia information systems and aligns with the broader developments in virtual and augmented realities.

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