A Robust Watermarking Method for Hyperspectral Images Based on Hybrid Attention Mechanism
De Li, Zhewei Zhang, Xuanyou Li, Xun Jin, Yanwei Wang · International Journal of Intelligent Systems · 2025
Because of the copyright issues of hyperspectral images continue to rise, in this paper, we propose to use a neural network–based watermarking model to protect the copyright. By applying normalization‐based attention module (NAM) to deep dispersed watermarking with synchronization and fusion (DWSF), a NDWSF model is proposed for robust hyperspectral image watermarking. It consists of encoding, decoding, discrimination, and attack modules. The encoding and decoding modules are used for embedding and extracting watermarks. Discrimination module is proposed for improving the quality of watermarked image. The discrimination module and the encoding module are in an adversarial relationship to motivate the encoder to generate watermarks with stronger invisibility. Attack module is employed between embedding and extraction to improve robustness against compression and noise and geometric attacks. In order to more effectively utilize image features for watermarking, a kind of hybrid attention mechanism is employed in embedding and extraction by adding NAM. Experimental results show that the loss convergence and stability in training is improved. The peak signal‐to‐noise ratio of the proposed method is 48.08 dB, higher than other methods about 2.5 dB. The bit error rate of the proposed method is less than 2.5% for various hybrid attacks, showing good robustness.