Robust Digital Watermarking for Remote Sensing Images Using Generative Adversarial Networks and Adaptive Channel Attention Mechanisms

Remote sensing images are vital for urban planning, resource management, and environmental protection. However, their proliferation exposes them to risks such as data theft, unauthorized modifications, and misuse, hindering secure information sharing and industry growth. To address these challenges, this paper introduces AWGAN, a robust watermarking method leveraging deep learning. AWGAN extracts deep image features to embed a robust and imperceptible digital watermark, ensuring security while maintaining data usability and visual fidelity. The method employs feature channel weighting to adaptively select key pixels for embedding, enhancing robustness and imperceptibility. It integrates multi-level feature extraction to fuse image features with the watermark, strengthening resilience. Additionally, adversarial training using a noise network layer and GAN discriminator improves resistance to distortions and ensures visual authenticity.