Data hiding is the procedure of encoding desired information into the cover
image to resist potential noises while ensuring the embedded image has few
perceptual perturbations from the original one. Recently, with the tremendous
successes gained by deep neural networks in various fields, the researches of
data hiding with deep learning models have attracted an increasing number of
attentions. In the data hiding task, each pixel of cover images should be
treated differently since they have divergent tolerabilities. The neglect of
considering the sensitivity of each pixel will inevitably affect the model
robustness for information hiding. Targeting this problem, we propose a novel
deep data hiding scheme with Inverse Gradient Attention (IGA), combing the
ideas of adversarial learning and attention mechanism to endow different
sensitivities for different pixels. With the proposed component, the model can
spotlight pixels with more robustness for data hiding. Empirically, extensive
experiments show that the proposed model outperforms the state-of-the-art
methods on two prevalent datasets under multiple evaluations. Besides, we
further identify and discuss the connections between the proposed inverse
gradient attention and high-frequency regions within images.

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