RW-PSCDLD: Robust watermarking for partial screen capture via decoupled localization and keypoint detection

The widespread use of digital displays and mobile cameras has increased the risk of information leakage through screen capture and photography. Partial screen capture, in which only a cropped, zoomed, or reframed region of the displayed content is observed, introduces geometric distortions, content loss, and camera-display artifacts. These degradations disrupt the spatial correspondence between watermark embedding and extraction, resulting in synchronization loss and unreliable watermark recovery. Existing localization-based watermarking methods often use the information-bearing watermark itself as the localization cue, making it difficult to independently optimize watermark fidelity and localization robustness. To address this limitation, we propose RW-PSCDLD, a decoupled deep watermarking framework specifically designed for partial screen-capture scenarios. RW-PSCDLD separates information watermark embedding from dedicated localization watermark embedding, allowing the two objectives to be optimized independently. Within this framework, we introduce the Homomorphic Localization Watermark based on Keypoint Detection (HLWKD). HLWKD employs a lightweight generator to embed Gaussian-template-constrained localization patterns at the corners of the information watermark region. A keypoint detector combining multi-scale feature extraction, deformable attention, and high-resolution prediction is designed to accurately localize these patterns under geometric and photometric distortions. Experiments under simulated and real-world screen-capture conditions demonstrate the effectiveness of the proposed method. Compared with a strong coupled baseline, RW-PSCDLD improves the PSNR by 1.44 dB within the watermark region and by 1.47 dB globally. Under combined attacks, it improves localization IoU and bit accuracy rate by 1.5 and 5.7 percentage points, respectively. The proposed method also achieves a favorable balance among visual quality, localization accuracy, watermark robustness, and computational efficiency.