Data hiding is the procedure of encoding desired information into a certain types of cover media (e.g. images) to resist potential noises for data recovery, while ensuring the embedded image has few perceptual perturbations. Recently, with the tremendous successes gained by deep neural networks in various fields, the research on data hiding with deep learning models has attracted an increasing amount of attentions. In deep data hiding models, to maximize the encoding capacity, each pixel of the cover image ought to be treated differently since they have different sensitivities w.r.t. visual quality. The neglecting to consider the sensitivity of each pixel inevitably affects the model's robustness for information hiding. In this paper, we propose a novel deep data hiding scheme with Inverse Gradient Attention (IGA), combining the idea of attention mechanism to endow different attention weights for different pixels. Equipped with the proposed modules, the model can spotlight pixels with more robustness for data hiding. Extensive experiments demonstrate that the proposed model outperforms the mainstream deep learning based data hiding methods on two prevalent datasets under multiple evaluation metrics. Besides, we further identify and discuss the connections between the proposed inverse gradient attention and high-frequency regions within images, which can serve as an informative reference to the deep data hiding research community. The codes are available at: https://github.com/hongleizhang/IGA.
翻译:数据隐藏程序是将理想信息编码成某类封面介质(例如图像),以抵制潜在噪音,促进数据恢复,同时确保嵌入图像的敏感度必然影响模型隐藏的稳健性。最近,随着深神经网络在各个领域取得的巨大成功,关于数据隐藏的深学习模型的研究吸引了越来越多的注意力。在深数据隐藏模型中,为了最大限度地提高编码能力,对封面图像的每个像素应区别对待,因为它们有不同的敏感度(例如图像),而同时又确保嵌入图像的敏感度不可避免地影响模型的稳健性。在本文中,我们建议与Inverse Graent Reative(IGA)一起采用新的深数据隐藏方案,将关注机制的理念结合到不同像素的不同偏重。在深数据隐藏模型中,模型可以以更坚固的隐藏数据。广泛的实验表明,拟议的模型超越了在多个评价指标中两种流行数据集的主流深层学习数据隐藏方法。此外,我们还可以在深度数据库中查找高频度数据库。