DualFocus GAN for Robust Watermarking in Transportation Cyber-Physical Systems

With the advancement of Transportation Cyber-Physical Systems (TCPS), information security has become increasingly critical. Invisible watermarking, which ensures reliable information traceability without compromising carrier quality, holds significant potential for TCPS. However, achieving high robustness in the real world while maintaining imperceptibility is a challenge. To address this, we propose DGWW (Dual-discriminator GAN-based WaveFusion Watermarking), a novel invisible watermarking method that balances robustness and imperceptibility. The GAN-based approach is well suited for TCPS, as it enables adaptive watermark embedding aligned with the dynamic and heterogeneous nature of transportation data, effectively handling diverse noise conditions and data types. DGWW integrates a WaveFusion Encoding Module, a Dual-Focus Discriminator, and a contrastive learning-based optimization strategy to enhance watermark embedding without degrading robustness. These components leverage multi-frequency information, assess local and global impacts on image quality, and guide model optimization. Experimental results show that DGWW outperforms state-of-the-art methods in visual quality and robustness under various noise conditions, offering a robust and scalable solution for image watermarking in TCPS environments. By maintaining data usability and strong resistance to noise attacks, DGWW advances digital watermarking in intelligent transportation systems.