A Geometric Distortion Immunized Deep Watermarking Framework with Robustness Generalizability

Linfeng Ma, Han Fang*, Tianyi Wei, Zijin Yang, Zehua Ma*, Weiming Zhang, Nenghai Yu ;

"Robustness is the most important property of watermarking schemes. In practice, the watermarking mechanism shall be robust to both geometric and non-geometric distortions. In deep learning-based watermarking frameworks, robustness can be ensured by end-to-end training with different noise layers. However, most of the current CNN-based watermarking frameworks, even trained with targeted distortions, cannot well adapt to geometric distortions due to the architectural design. Since the traditional convolutional layer’s position structure is relatively fixed, it lacks the flexibility to capture the influence of geometric distortion, making it difficult to train for corresponding robustness. To address such limitations, we propose a Swin Transformer and Deformable Convolutional Network (DCN)-based watermark model backbone. The attention mechanism and the deformable convolutional window effectively improve the feature processing flexibility, greatly enhancing the robustness, especially for geometric distortions. Besides, for non-geometric distortions, aiming at improving the generalizability for more distortions, we also provide a distortion-style-ensembled noise layer, including an image encoder, an image decoder, and distortion-style layers that can effectively simulate styles of different kinds of distortions. Then we can simply train our watermark model with the proposed noise layer for overall robustness. Experiments illustrate the superiority of our method compared to existing state-of-the-art (SOTA) works, such as the 100.00% watermark extraction accuracy under almost all tested geometric distortions."