GCMark: Robust Image Watermarking with Gated Feature Selection and Cover-Guided Expansion

Deep learning-based methods have achieved promising performance in image watermarking tasks due to their powerful capability to fully exploit the rich information present in images, which is crucial for ensuring watermark robustness. Although existing methods have improved robustness against various distortions, directly using deep neural networks for feature extraction and watermark expansion often introduces irrelevant and redundant features, thereby limiting the watermarking model’s imperceptibility and robustness. To address these limitations, in this paper, we introduce a robust image watermarking framework (GCMark) based on Gated Feature Selection and Cover-Guided Expansion, which consists of two key components: (1) the Dual-Stage Gated Modulation Block (DSGMB) that serves as the core backbone of both the encoder and decoder, adaptively suppressing irrelevant or redundant activations to enable more precise watermark embedding and extraction; (2) the Cover-Guided Message Expansion Block (CGMEB), which exploits the cover image’s structural features to guide watermark message expansion. By promoting a structurally consistent and well-balanced fusion between the watermark and host image, this implicit symmetry facilitates stable watermark propagation and enhances resilience against various distortions without introducing noticeable visual artifacts. Extensive experimental results demonstrate that the proposed GCMark framework outperforms existing methods with respect to both robustness and imperceptibility.