Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DBMARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible neural networks (INN) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through the effective neural network based message processor, synergized with the powerful information embedding and extraction abilities of INN to achieve higher robustness and invisibility. The powerful learning ability of neural networks enables the message processor to adapt to various distortions. In addition, we propose to embed the watermark information in the discrete wavelet transform (DWT) domain and design low-low (LL) sub-band loss to enhance invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions such as dropout, cropout, crop, Gaussian filter, and JPEG compression.
翻译:图像标记是一种技术,将信息隐藏在能够承受扭曲的图像中,同时要求编码成像的图像与原始图像在概念上完全相同。基于深神经网络(DNN)的近期工作在数字水标记方面取得了令人印象深刻的进展。在各种扭曲下,更高的稳健性是长期追求数字图像水标记方法。在本文中,我们提议DBMARK,这是一个全新的端对端数字图像标记框架,以深入增强DNN的图像标记的稳健性。关键的新颖之处是不可辨别的神经网络(INN)和有效水标记特征生成的协同作用。这个框架通过有效的神经网络信息处理器(DNNN)生成了冗余和差校正能力。与数字图像标记的强大信息嵌入和提取能力相结合,以实现更高的稳健性和不可忽视性。我们提议将水标记信息输入离散的电波转换(DWT)域网域和有效水标记特性生成的协同作用。这个框架通过有效的神经网络信息处理器(DNNNNNN)生成了冗余和错误校准能力,通过有效的电网络处理器生成信息处理器生成出冗余和错误校正功能生成了水标记特性。它生成了水标记特性,与水标记特性,并结合了水标记特性生成了水标记特性,从而加强了了IN网网固化了电压式的模型化,从而,从而加强了了各种作物变压,从而展示了各种作物变压,从而提高了了各种作物变压变压变压框架,从而,从而加强了了作物的压。