Image watermarking optimization based on dual attention module mechanism
Xinchen Leng, Xin Heng, Jingjie Wang · 2024
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.