Image Hash Combining Quaternion Laguerre Moments and Three-Dimensional Structures

doi:10.19678/j.issn.1000-3428.0066413

Abstract

Abstract:

Currently, several algorithms used in image hashing can only handle grayscale images. In this study, a hashing algorithm based on quaternion Laguerre moments and a three-dimensional energy structure is proposed to improve the range of application and performance of the image hashing algorithm, particularly its robustness against rotation attacks. First, the input color image is preprocessed, multiscale fusion is performed, and the Laguerre moment coefficients are extracted from the fused image as the global features of the image. The energy information of the fused image is used to establish a model in the YCbCr color space, and the angle between the energy peak and valley points connected with the horizontal plane at different viewpoints in the three-dimensional(3D) model is selected as the local structure feature. The features with rotational invariance are extracted by the positions of the near and far points on the specific points and each contour of the 3D model. Finally, the global and 3D structural features are combined, quantized, and encrypted to generate hash sequences. Finally, the global and 3D structural features are combined to quantify and encrypt the generated hash sequences. The results show that the subject operating characteristic curve exhibits a correct acceptance rate of 0.999 2 when the error reception rate is 0. A Hash sequence length of 120 bit possesses optimal compactness, and the average computation time reaches 0.097 9 s. In copy detection experiments, the algorithm performs multiple extraction experiments with an average check-all rate, and the average check rate and accuracy rate of the algorithm for multiple extraction experiments are higher than 95.83%.

Key words: quaternion Laguerre moments, image Hash, image energy, three-dimensional structural features, copy detection

摘要：

目前图像哈希领域中许多算法只能处理灰度图像, 为扩大算法适用范围, 同时提高图像哈希算法的性能与旋转攻击的鲁棒性, 提出基于四元数拉盖尔矩和三维能量结构的哈希算法。首先对输入的彩色图像进行预处理与多尺度融合处理, 将融合图像提取的拉盖尔矩系数作为图像的全局特征, 同时在YCbCr颜色空间中利用融合图像的能量信息建立模型, 选取三维模型中不同视角下的能量峰值和谷值点连线与水平面的夹角作为局部结构特征; 然后根据特定点与三维模型各条等高线上近点和远点的位置提取具有旋转不变性的特征; 最后结合全局特征和三维结构特征, 量化并加密生成哈希序列。实验结果表明, 该算法在鲁棒性与区别性之间有更好的平衡, 受试者工作特征曲线错误接受率为0时的正确接受率达到0.999 2。当哈希序列长度为120 bit时具备最优的紧凑性, 平均计算时间为0.097 9 s。在拷贝检测实验中, 该算法进行多次抽取实验的平均查全率和查准率均在95.83%以上。

关键词: 四元数拉盖尔矩, 图像哈希, 图像能量, 三维结构特征, 拷贝检测

Jie BAI, Yan ZHAO. Image Hash Combining Quaternion Laguerre Moments and Three-Dimensional Structures[J]. Computer Engineering, 2024, 50(4): 208-218.

白杰, 赵琰. 结合四元数拉盖尔矩和三维结构的图像哈希[J]. 计算机工程, 2024, 50(4): 208-218.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0066413

http://www.ecice06.com/EN/Y2024/V50/I4/208

Figures/Tables 21

Fig.1 Procedure of Hash algorithm

Fig.2 Three-dimensional energy model and peak and valley value curves

Fig.3 Schematic diagram of the contour model

Fig.4 Images of robustness experiments

Fig.5 Results of robustness experiments

Fig.6 Hamming distance distribution

Fig.7 ROC curves for each block size and order

Fig.8 ROC curves before and after multi-scale fusion

Fig.9 Robustness comparison before and after adding contour features

Fig.10 ROC curve comparison of different algorithms

Fig.11 Comparison of ROC curve of each algorithms under different N values

Fig.12 Comparison of P-R curve of different algorithms

References 29

1	OUYANG J L, COATRIEUX G, SHU H Z. Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform. Digital Signal Processing, 2015, 41, 98- 109. doi: 10.1016/j.dsp.2015.03.006
2	LEI Y Q, WANG Y G, HUANG J W. Robust image hash in Radon transform domain for authentication. Signal Processing, 2011, 26(6): 280- 288.
3	TANG Z J, HUANG Z Q, ZHANG X Q, et al. Robust image hashing with multidimensional scaling. Signal Processing, 2017, 137, 240- 250. doi: 10.1016/j.sigpro.2017.02.008
4	TANG Z J, YANG F, HUANG L Y, et al. Robust image hashing with dominant DCT coefficients. Optik, 2014, 125(18): 5102- 5107. doi: 10.1016/j.ijleo.2014.05.015
5	QIN C, CHEN X Q, DONG J, et al. Perceptual image hashing with selective sampling for salient structure features. Displays, 2016, 45, 26- 37. doi: 10.1016/j.displa.2016.09.003
6	OU Y, RHEE K H. A key-dependent secure image hashing scheme by using Radon transform[C]//Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems. Washington D. C. , USA: IEEE Press, 2010: 595-598.
7	SHEN Q, ZHAO Y. Statistical feature hashing based on wavelet decomposition. Journal of Applied Sciences, 2018, 36(2): 247- 254. doi: 10.3969/j.issn.0255-8297.2018.02.004
8	VADLAMUDI L N, VADDELLA R P V, DEVARA V. Robust image hashing using SIFT feature points and DWT approximation coefficients. ICT Express, 2018, 4(3): 154- 159. doi: 10.1016/j.icte.2017.12.004
9	LI X W, XIA X Z. Robust image hashing based on content structure diagram. Journal of Applied Sciences, 2016, 34(6): 691- 701. doi: 10.3969/j.issn.0255-8297.2016.06.005
10	TANG Z J, RUAN L L, QIN C, et al. Robust image hashing with embedding vector variance of LLE. Digital Signal Processing, 2015, 43, 17- 27. doi: 10.1016/j.dsp.2015.05.002
11	TANG Z J, ZHANG X Q, ZHANG S C. Robust perceptual image hashing based on ring partition and NMF. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3): 711- 724. doi: 10.1109/TKDE.2013.45
12	DAVARZANI R, MOZAFFARI S, YAGHMAIE K. Perceptual image hashing using center-symmetric local binary patterns. Multimedia Tools and Applications, 2016, 75(8): 4639- 4667. doi: 10.1007/s11042-015-2496-6
13	SHEN Q, ZHAO Y. Image hash algorithm for copy detection. Application Research of Computers, 2019, 36(2): 611- 614.
14	QIN C, HU Y C, YAO H, et al. Perceptual image hashing based on weber local binary pattern and color angle representation. IEEE Access, 2019, 7, 45460- 45471. doi: 10.1109/ACCESS.2019.2908029
15	TANG Z J, ZHANG X Q, LI X X, et al. Robust image hashing with ring partition and invariant vector distance. IEEE Transactions on Information Forensics and Security, 2015, 11(1): 200- 214.
16	TANG Z J, ZHANG X Q, DAI X, et al. Robust image hash function using local color features. International Journal of Electronics and Communications, 2013, 67(8): 717- 722. doi: 10.1016/j.aeue.2013.02.009
17	SHEN Q, ZHAO Y. Perceptual hashing for color image based on color opponent component and quadtree structure. Signal Processing, 2020, 166, 107244. doi: 10.1016/j.sigpro.2019.107244
18	ZHAO Y, WANG S Z. Perpetual image hashing based on conformal mapping and Zernike moments. Journal of Applied Sciences, 2012, 30(1): 75- 81.
19	赵秀锋, 魏伟一, 陈金寿, 等. 基于自适应四元数奇异值分解的图像拼接检测. 计算机工程, 2022, 48(4): 223-230, 239. URL
	ZHAO X F, WEI W Y, CHEN J S, et al. Image splicing detection based on adaptive quaternion singular value decomposition. Computer Engineering, 2022, 48(4): 223-230, 239. URL
20	赫晓慧, 宋定君, 李盼乐, 等. 融合多尺度特征的遥感影像道路提取方法. 计算机工程, 2022, 48(8): 196- 205. URL
	HE X H, SONG D J, LI P L, et al. Remote sensing image road extraction method combined with multi-scale features. Computer Engineering, 2022, 48(8): 196- 205. URL
21	PAN B J, LI Y H, ZHU H Q. Image description using radial associated laguerre moments. Journal of ICT Research and Applications, 2015, 9(1): 1- 19. doi: 10.5614/itbj.ict.res.appl.2015.9.1.1
22	李旭东. 基于图像能量和量化的公开数字水印算法. 中北大学学报(自然科学版), 2007, 28(5): 458- 461. doi: 10.3969/j.issn.1673-3193.2007.05.018
	LI X D. Publicdigital watermark algorithm based on image energy and quantization. Journal of North University of China(Natural Science Edition), 2007, 28(5): 458- 461. doi: 10.3969/j.issn.1673-3193.2007.05.018
23	University of Washington. Ground truth database[EB/OL]. [2022-10-30]. http://www.cs.washington.edu/research/image database/groundtruth/.
24	REDMOD J. Pascal VOC dataset mirrir[EB/OL]. [2022-10-30]. https://pjreddie.com/projects/pascal-voc-dataset-mirror/.
25	FAWCETT T. An introduction to ROC analysis. Pattern Recognition Letters, 2006, 27(8): 861- 874. doi: 10.1016/j.patrec.2005.10.010
26	MA L S, ZHAO Y. Image hashing algorithm based on QBFM moments and three-dimensional structure. Application Research of Computers, 2022, 39(3): 949- 955.
27	TANG Z J, ZHANG H Y, PUN C M, et al. Robust image hashing with visual attention model and invariant moments. IET Image Processing, 2020, 14(5): 901- 908. doi: 10.1049/iet-ipr.2019.1157
28	TANG Z J, YU M Z, YAO H, et al. Robust image hashing with singular values of quaternion SVD. The Computer Journal, 2021, 64(11): 1656- 1671. doi: 10.1093/comjnl/bxz127
29	MÜLLER H, MÜLLER W, SQUIRE D M, et al. Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recognition Letters, 2001, 22(5): 593- 601. doi: 10.1016/S0167-8655(00)00118-5

[1]	SHU Fang, CUI Shao-Ban, GAO Zhen-Pan. Video Copy Detection Based on Shot Duration and Texture Information [J]. Computer Engineering, 2013, 39(2): 304-310.
[2]	TANG Yu-Yuan, OU Yang-Jian-Quan. Rapid Video Copy Detection Method Based on Video Fingerprint [J]. Computer Engineering, 2011, 37(6): 277-279.
[3]	LIU Hong; WEN Zhao-hui; WANG Ye;. Study on Content-based Video Copy Detection [J]. Computer Engineering, 2010, 36(7): 227-229.
[4]	NIE Gui-hua; FU Zhi-chao; CHEN Dong-lin; LIU Ping-feng. Ontology-based Thesis Copy Detection System [J]. Computer Engineering, 2009, 35(6): 79-81.

Please choose a citation manager

Content to export