Digital Watermarking via Inverse Gradient Attention

Watermarking is the procedure of encoding desired information into an image to resist potential noises while ensuring the embedded image has little perceptual perturbations from the original image. However, the neglect of considering the pixel importance within the cover image of deep neural models will inevitably affect the model robustness for digital watermarking. Targeting at the problem, in this paper, we propose a novel deep watermarking scheme with Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and attention mechanism to endow different importance to different pixels. With the proposed method, the model is able to spotlight pixels with more robustness for embedding data. Empirically, extensive experiments show that the proposed model outperforms the state-of-the-art methods on two prevalent datasets under multiple settings. Besides, we further identify and discuss the connections between the proposed inverse gradient attention with high-frequency regions within images.