FasterCReW: Performance or Efficiency? A Lightweight Conditional Residual DNN-Based Watermarking Based on FasterNet
Baowei Wang, Jianbo Zhang, Yufeng Wu, Qi Ming Cui · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Deep neural networks (DNNs) based watermarking algorithms have made significant strides in recent years. However, existing methods either demand substantial resources for image feature extraction during watermark embedding, sacrificing efficiency, or completely neglect image texture information, resulting in suboptimal performance. Moreover, current algorithms struggle with real-time watermark extraction. To address these limitations, we propose a lightweight conditional residual watermarking (CReW) architecture. Specifically, CReW employs a Conditional Generative Adversarial Network (CGAN) framework to generate an adaptive residual image guided by the structure of the cover image, which is decoupled from the network to reduce computational complexity. This design enables CReW to achieve an optimal balance between performance and efficiency. Additionally, by directly optimizing the residual image to capture variations in watermark behavior under distortion, CReW significantly enhances robustness. Furthermore, we design redundancy coding blocks to increase the mutual information of the watermark, along with a patch-level discriminator to improve local patch discrimination, thereby further enhancing image quality. Finally, by reducing channel redundancy and leveraging FasterNet, we developed a low-complexity network architecture, FasterCReW, which facilitates real-time watermark embedding and extraction. Extensive experimental results demonstrate that, despite having$36 \times $fewer network parameters and$30\times $fewer floating point operations (FLOPs) than Adaptor, FasterCReW exhibits excellent robustness against distortions such as cropout, JPEG compression, and Gaussian noise. Furthermore, FasterCReW significantly outperforms other existing DNN-based watermarking algorithms in terms of running speed, achieving an$8\times $speed increase over UDH and a$28\times $increase over Adaptor on an Intel Core i7-8750H CPU.