Paper
10 September 2024 Image watermarking optimization based on dual attention module mechanism
Xinchen Leng, Xin Heng, Jingjie Wang
Author Affiliations +
Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325703 (2024) https://doi.org/10.1117/12.3044257
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China
Abstract
The integration of deep learning methodologies within the realm of digital watermarking has assumed a pivotal role in safeguarding the intellectual property rights associated with images within contemporary contexts. By leveraging an end-to-end architecture comprising noise layers and codec components, the resilience of watermarks across diverse environmental conditions is upheld. To augment the fidelity and perceptibility of watermark representations, a dual encryption paradigm is embraced, integrating a dual attention framework alongside a Least Significant Bit (LSB) processing module. This encoding scheme amalgamates both spatial and channel attention mechanisms, thereby fortifying the resilience of the model. The channel attention mechanism enables precise embedding of watermarks within critical image channels, while the spatial attention mechanism facilitates seamless integration within intricate textural regions. Furthermore, the LSB method is employed for secondary encryption of image data, ensuring both concealment and robustness of the watermarking technique.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xinchen Leng, Xin Heng, and Jingjie Wang "Image watermarking optimization based on dual attention module mechanism", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325703 (10 September 2024); https://doi.org/10.1117/12.3044257
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KEYWORDS
Digital watermarking

Image encryption

Image processing

Feature extraction

Digital imaging

Image enhancement

Deep learning

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