Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (1): 174-179.doi: 10.13229/j.cnki.jdxbgxb20200681

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High-capacity and robust image watermarking algorithm

Xi-yan LI1,2(),Xia-bing ZHOU3(),Zheng LIU1,4   

  1. 1.State Key Laboratory of Mathematical Engineering and Advanced Computing,PLA Information Engineering University,Zhengzhou 450001,China
    2.School of Information Science and Technology,Hainan Normal University,Haikou 571158,China
    3.School of Computer Science and Technology,Soochow University,Suzhou 215006,China
    4.School of Information on Engineering,Henan University of Animal Husbandry and Economy,Zhengzhou 450001,China
  • Received:2020-06-28 Online:2022-01-01 Published:2022-01-14
  • Contact: Xia-bing ZHOU E-mail:xiyanli2006@163.com;zhouxiabing@suda.edu.cn

Abstract:

This paper presents a reliable digital watermarking technique. For the first time, the watermark image is preprocessed by halftone and quad tree techniques, and the position information of the content is extracted as the actual embedded value. The cover image is processed by Discrete Wavelet Transform (DWT), and then the high frequency sub-band is divided into blocks, sized by . The blocks were processed by an optimal Discrete Cosine Transform (DCT) psychovisual threshold. Embedding process in this paper utilizes certain frequency regions of DCT such that insertion of watermark bits causes the least image distortion. The information was extracted by comparing certain coefficients based on some rules. According to the information, the watermark image can be reconstructed. The experimental results show that our algorithm achieves higher capacity and robustness than the existing schemes. The watermark extraction produces high image quality after cutting, noise, filter and JPEG compression attacks.

Key words: digital watermarking, halftone, quad tree, discrete wavelet transforms, discrete cosine transforms

CLC Number: 

  • TP309

Fig.1

QR code content extraction process"

Fig.2

Word watermark content extraction process"

Table 1

PSNR and NC values when not under attack"

水 印PSNRNC
“中”字水印60.40230.9972
预处理“中”字水印62.17870.9966
QR码60.48760.9972
预处理QR码52.59890.9965

Table 2

PSNR and NC values under noise attack"

噪 声PSNRNC
椒盐噪声(0.01)5.71720.9972
椒盐噪声(0.05)5.71590.9843
高斯噪声(0.01)60.84100.9411
高斯噪声(0.02)60.83450.9369
高斯噪声(0.03)60.83300.9301
高斯噪声(0.04)60.81630.9295

Table 3

PSNR and NC values under JPEG compression attack"

压缩因子PSNRNC
9036.87650.9998
7035.11940.9998
5030.60120.9985
3030.00100.7774

Table 4

NC values under filter attack"

滤波攻击NC
高斯低通过滤(3×3)0.9726
中值过滤(3×3)0.9872

Table 5

Comparison between proposed method and Ref.[8]without attack"

载体图像文献[8本文算法
PSNRNCPSNRNC
Lena45.6890.99460.40230.9972
Sailboat45.7420.99455.13830.9972
Airplane45.9260.99366.81420.9972
Baboon44.5460.99562.69180.9966
Pepper45.5330.99452.59890.9965
Man45.3000.99558.46190.9972
Average45.4560.99459.35120.9970

Table 6

Comparison between proposed method and Ref.[8] under different types of attack"

攻击类型文献[8本文算法
中值过滤[3×3]0.98540.9872
椒盐噪声(0.01)0.83140.9972
高斯噪声(0.001)0.93930.9411
椒盐噪声(0.003)和中值滤波[3×3]0.95630.9584
JPEG(30)0.77690.7774
JPEG(50)0.99900.9985
JPEG(70)1.00000.9998
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