DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking

Yimeng Zhao, Chengyou Wang (), Xiao Zhou and Zhiliang Qin
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Yimeng Zhao: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Chengyou Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Xiao Zhou: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Zhiliang Qin: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China

Mathematics, 2022, vol. 11, issue 1, 1-16

Abstract: At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the field of watermarking. In this paper, we propose a deep learning and attention network for robust image watermarking (DARI-Mark). The framework includes four parts: an attention network, a watermark embedding network, a watermark extraction network, and an attack layer. The attention network used in this paper is the channel and spatial attention network, which calculates attention weights along two dimensions, channel and spatial, respectively, assigns different weights to pixels in different channels at different positions and is applied in the watermark embedding and watermark extraction stages. Through end-to-end training, the attention network can locate nonsignificant areas that are insensitive to the human eye and assign greater weights during watermark embedding, and the watermark embedding network selects this region to embed the watermark and improve the imperceptibility. In watermark extraction, by setting the loss function, larger weights can be assigned to watermark-containing features and small weights to noisy signals, so that the watermark extraction network focuses on features about the watermark and suppresses noisy signals in the attacked image to improve robustness. To avoid the phenomenon of gradient disappearance or explosion when the network is deep, both the embedding network and the extraction network have added residual modules. Experiments show that DARI-Mark can embed the watermark without affecting human subjective perception and that it has good robustness. Compared with other state-of-the-art watermarking methods, the proposed framework is more robust to JPEG compression, sharpening, cropping, and noise attacks.

Keywords: image watermarking; deep learning; attention network; imperceptibility; robustness (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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