Research on image digital watermarking optimization algorithm under virtual reality technology

Yi Zhang; Xiao-Li Ma

doi:10.3934/dcdss.2019098

Article Contents

2019, Volume 12, Issue 4&5: 1427-1440. Doi: 10.3934/dcdss.2019098 open access

This issue Previous Article Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment Next Article An algorithm for reversible information hiding of encrypted medical images in homomorphic encrypted domain

Research on image digital watermarking optimization algorithm under virtual reality technology

Yi Zhang^1, , and
Xiao-Li Ma^2,

1.
Language Laboratory, Department of Foreign Languages, Anhui Jianzhu University, Hefei, China
2.
Department of Foreign Languages, Anhui Jianzhu University, Hefei, China

* Corresponding author: Yi Zhang
* Corresponding author: Yi Zhang

Received: June 30, 2017

Revised: November 30, 2017

Published: March 2019

Abstract Full Text(HTML) Figure(5) / Table(3) Related Papers Cited by

Abstract

Aiming at the shortcomings of current algorithms due to the fixed steps, which is easy to fall into local optimum, with robustness and poor transparency, and cannot be balanced against various common attacks, an optimization algorithm of digital image watermarking algorithm based on Drosophila was proposed. In the support of the virtual reality technology, the original color host image was transformed from the RGB space to YCrCb space, and the pixel block of the Y component was divided into a certain size; according to the principle of forming DC coefficients in the DCT domain, the DC coefficient of each block is calculated directly in the airspace, and the amount of modification for each DC coefficient is determined based on the watermark information and the quantization step size; according to the distribution characteristics of DC coefficient, watermarks are embedded directly in the airspace; the type of digital watermarking and digital watermarking pretreatment methods were determined by using Drosophila optimization algorithm. At the same time, digital watermark embedding, extraction rules and initial steps were selected and identified. The Drosophila optimization algorithm with step size reduces the balance between global and local search ability, which makes up for the shortcomings of traditional algorithm. The experimental results showed that the proposed algorithm can effectively balance the invisibility and robustness of the watermark, and can resist all kinds of common attacks, which with a better visual extraction effect.

Keywords:

Mathematics Subject Classification: 60J67.

Citation:

FullText(HTML)

Figure 1. Image digital watermarking preprocessing under virtual reality technology

Download: Full-size image PowerPoint slide

Figure 2. Color images of the original carrier

Download: Full-size image PowerPoint slide

Figure 3. Original watermark image and scrambled watermark image

Download: Full-size image PowerPoint slide

Figure 4. Watermarked color image

Download: Full-size image PowerPoint slide

Figure 5. Watermarking effects and watermarking extraction effects after different attacks

Download: Full-size image PowerPoint slide

Table 1. PSNR values of the original and watermarked images for each attack

Attack mode	Figure 4 (a)	Figure 4 (b)
No attack	86.4859	87.0142
Rotation (5 degrees)	72.6435	73.8816
Image scaling (1/2)	77.5878	75.3243
JPEG compression (90)	83.6266	84.1118
Cropping	62.5677	64.2054

| Show Table

DownLoad: CSV

Table 2. NC value of the original watermark and extracted watermark image under various attacks

Attack mode	Figure 4 (a)	Figure 4 (b)
No attack	1.0000	1.0000
Salt and pepper noise (0.05)	0.9987	0.9887
Median filter ($5 \times 5$)	0.9654	0.9574
Rotation (5 degrees)	0.9613	0.9788
Image scaling (1/2)	0.9527	0.9755
JPEG compression (90)	0.9774	0.9802
Cropping	0.9997	1.0000

| Show Table

DownLoad: CSV

Table 3. Experimental results of two watermarked images under varying degrees of attack

Attack mode Vector image		Figure 2 (a) NC value	Figure 2 (b) NC value
JPEG compression (quality factor)	90	0.9778	0.9805
	70	0.9594	0.9698
	50	0.9473	0.9587
	30	0.9372	0.9583
Salt and pepper noise (intensity)	0.05	0.9877	0.9886
	0.10	0.9602	0.9734
	0.15	0.9571	0.9722
	0.20	0.9501	0.9579

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	A. A., M. A. and A. A., Crypto-watermarking of transmitted medical images, Journal of Digital Imaging, 26-38.
[2]	A. M. Abbas, Block-based svd image watermarking in spatial and transform domains, International Journal of Electronics, 102 (2015), 1091-1113.
[3]	A. M. Abdelhakim, H. I. Saleh and A. M. Nassar, Quality metric-based fitness function for robust watermarking optimisation with bees algorithm, Iet Image Processing, 10 (2016), 247-252.
[4]	M. Andalibi and D. M. Chandler, Digital image watermarking via adaptive logo texturization, IEEE Transactions on Image Processing, 24 (2015), 5060-5073.
[5]	X. D., C. H. K. and Z. H. Y., A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map, Chinese Physics B, 24 (2015), 198-206.
[6]	Y. Guo, B. Z. Li and N. Goel, Optimised blind image watermarking method based on firefly algorithm in dwt-qr transform domain, Iet Image Processing, 11 (2017), 406-415.
[7]	W. H. D., L. Y. F. and S. T., An image quality assessment method using human visual characteristics, 567-573.
[8]	C. J., Research on digital watermarking algorithm based on compressed sensing, in Bulletin of Science and Technology, 11 (2016), 137-141.
[9]	S. Liu, Z. Pan and H. Song, Digital image watermarking method based on dct and fractal encoding, Iet Image Processing, 11 (2017), 815-821.
[10]	R. R. A. Lubis, Analisis kombinasi algoritma watermarking modified least significant bit dengan least significant bit +1, Critical Care, 14 (2015), 1-1.
[11]	L. Mao, W. Guiwu, F. Alsaadi, T. Hayat and A. Alsaedi, Bipolar 2-tuple linguistic aggregation operators in multiple attribute decision making, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 33 (2017), 1197-1207.
[12]	L. Narkedamilly, V. P. Evani and S. K. Samayamantula, Discrete multiwavelet--based video watermarking scheme using surf, Etri Journal, 37 (2015), 595-605.
[13]	K. Praveen, M. Sethumadhavan and R. Krishnan, Visual cryptographic schemes using combined boolean operations, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 413-437. doi: 10.1080/09720529.2015.1086067.
[14]	Z. Qu, Z. Cheng, M. Luo and W. Liu, A robust quantum watermark algorithm based on quantum log-polar images, International Journal of Theoretical Physics, 56 (2017), 3460-3476. doi: 10.1007/s10773-017-3512-6.
[15]	K. S. E., Science teachers' conceptualizations and implications for the development of the professional development programs, Eurasia Journal of Mathematics Science and Technology Education, 13 (2017), 3301-3314.
[16]	L. B. Si, Regional cooperation efficiency evaluation of equipment manufacturing industry based on dea method: Empirical analysis of beijing-tianjin-hebei region and yangtze river delta region, Journal of Interdisciplinary Mathematics, 20 (2017), 281-293.
[17]	B. Tenner, Discrete mathematics and theoretical computer science, Discrete Mathematics and Theoretical Computer Science, 369-382.
[18]	M. A. Ting, D. P. Gao and N. T. Chen, Optimization and simulation of anti-attack method for composite color digital watermark image, Computer Simulation, 418-422.
[19]	D. C. Wang, C. C. Tian, B. J. Chen and Y. H. Tian, Dual watermarking for color images based on 4d quaternion frequency domain, Journal of Jilin University, 45 (2015), 1336-1346.
[20]	Y. Wang, J. Liu, Y. Yang, D. Ma and R. Liu, 3d model watermarking algorithm robust to geometric attacks, Iet Image Processing, 11 (2017), 822-832.