Shrinkage and Redundant Feature Elimination Network-Based Robust Image Zero-Watermarking

被引:3
作者
He, Lingqiang [1 ,2 ]
He, Zhouyan [1 ,2 ]
Luo, Ting [1 ]
Song, Yang [1 ]
机构
[1] Ningbo Univ, Coll Sci & Technol, Ningbo 315000, Peoples R China
[2] Ningbo Univ, Fac Informat Sci & Engn, Ningbo 315211, Peoples R China
来源
SYMMETRY-BASEL | 2023年 / 15卷 / 05期
基金
浙江省自然科学基金;
关键词
zero-watermarking; dense connection; shrinkage module; redundant feature elimination; SCHEME; ALGORITHM; TRANSFORM; CNN;
D O I
10.3390/sym15050964
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
To address the contradiction between watermarking robustness and imperceptibility, a zero-watermarking method based on shrinkage and a redundant feature elimination network (SRFENet) is proposed in this paper. First, in order to have the capability of resisting different image attacks, a dense connection was used to extract shallow and deep features from different convolutional layers. Secondly, to reduce unimportant information for robustness and uniqueness, in SRFENet, a shrinkage module was utilized by automatically learning the threshold of each feature channel. Then, to enhance watermarking uniqueness, a redundant feature elimination module was designed to reduce redundant information for the remaining valid features by learning the weights of inter-feature and intra-feature. In order to increase watermarking robustness further, noised images were generated for training. Finally, an extracted feature map from SRFENet was used to construct a zero-watermark. Furthermore, a zero-watermark from the noised image was generated for copyright verification, which is symmetrical to the process of zero-watermark construction from the original image. The experimental results showed that the proposed zero-watermarking method was robust to different single-image attacks (average BER is 0.0218) and hybrid image attacks (average NC is 0.9551), proving the significant generalization ability to resist different attacks. Compared with existing zero-watermarking methods, the proposed method is more robust since it extracts the main image features via learning a large number of different images for zero-watermark construction.
引用
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页数:16
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