Abstract
We propose a novel perceptual hashing based on salient region and a nonnegative matrix factorization (NMF) in this paper. Firstly, the input image is standardized and processed with low-pass filtering. Secondly, the salient region is extracted from the obtained preprocessed image. The minimum bounding rectangle of each salient region is extracted, and the pixels in all rectangles are rearranged to form a secondary image, then the preprocessed image is decomposed by NMF to obtain the coefficient matrix as the final image hash. The algorithm is robust to general content-preserving manipulations through the experiment. The proposed algorithm outflanks some best in the performances of perceptual robustness and discrimination indicated by identification accuracy performances.
Supported by the National Natural Science Foundation of China (Grant Number: 61702224). The Special Funds of Heilongjiang University of the Fundamental Research Funds for the Heilongjiang Province (RCCXYJ201811). Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201904.).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Swaminathan, A., Mao, Y., Wu, M.: Robust and secure image hashing. IEEE Trans. Inf. Forensics Secur. 1, 215–230 (2006)
Ahmed, F., Siyal, M.Y., Abbas, V.U.: A secure and robust hash-based scheme for image authentication. Sig. Process. 90, 1456–1470 (2010)
**ang, S., Kim, H.-J., Huang, J.: Histogram-based image hashing scheme robust against geometric deformations. In: Proceedings of the 9th Workshop on Multimedia and Security, pp. 121–128 (2007)
Gharde, N.D., Thounaojam, D.M., Soni, B., Biswas, S.K.: Robust perceptual image hashing using fuzzy color histogram. Multimedia Tools Appl. 77, 30815–30840 (2018)
Tang, Z., Huang, L., Dai, Y., Yang, F.: Robust image hashing based on multiple histograms. Int. J. Digital Content Technol. Appl. 6, 39 (2012)
Choi, Y.S., Park, J.H.: Image hash generation method using hierarchical histogram. Multimedia Tools Appl. 61, 181–194 (2012)
Tang, Z., Dai, Y., Zhang, X., Zhang, S.: Perceptual image hashing with histogram of color vector angles. In: International Conference on Active Media Technology, pp. 237–246 (2012)
Hamon, K., Schmucker, M., Zhou, X.: Histogram-based perceptual hashing for minimally changing video sequences. 2006 Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (AXMEDIS’06), pp. 236–241 (2006)
Lefebvre, F., Czyz, J., Macq, B.: A robust soft hash algorithm for digital image signature. In: Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429), 2, II–495 (2003)
Lefebvre, F., Macq, B., Legat, J.-D.: RASH: Radon soft hash algorithm. In: 2002 11th European Signal Processing Conference, pp. 1–4 (2002)
Kozat, S.S., Venkatesan, R., Mihçak, M.K.: Robust perceptual image hashing via matrix invariants. In: 2004 International Conference on Image Processing, 2004, pp. 3443–3446. ICIP’04. 5 (2004)
Khelifi, F., Jiang, J.: Analysis of the security of perceptual image hashing based on non-negative matrix factorization. IEEE Sig. Process. Lett. 17, 43–46 (2009)
Tang, Z., Zhang, X., Zhang, S.: Robust perceptual image hashing based on ring partition and NMF. IEEE Trans. Knowl. Data Eng. 26, 711–724 (2013)
Lv, X., Wang, Z.J.: Perceptual image hashing based on shape contexts and local feature points. IEEE Trans. Inf. Forensics Secur. 7, 1081–1093 (2012)
Paul, M., Karsh, R.K., Talukdar, F.A.: Image hashing based on shape context and speeded up robust features (SURF). In: 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 464–468 (2019)
Tu, W.-C., He, S., Yang, Q., Chien, S.-Y.: Real-time salient object detection with a minimum spanning tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2334-2342 (2016)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1597–1064 (2009)
Tang, Z., Zhang, X., Li, X., Zhang, S.: Robust image hashing with ring partition and invariant vector distance. IEEE Trans. Inf. Forensics Secur. 11, 200–214 (2015)
Hamid, H., Ahmed, F., Ahmad, J.: Robust image hashing scheme using laplacian pyramids. Comput. Electr. Eng. 84, 106648 (2020)
Acknowledgements
We would like to thank anonymous reviewers for their helpful comments and suggestions, and their comments and suggestions help us to improve this paper’s quality. This work is supported by the National Natural Science Foundation of China (Grant Number: 61702224). The Special Funds of Heilongjiang University of the Fundamental Research Funds for the Heilongjiang Province (RCCXYJ201811). Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201904.)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, X., Cui, C., Wang, S. (2022). Perceptual Hashing Based on Salient Region and NMF. In: Chu, SC., Chen, SH., Meng, Z., Ryu, K.H., Tsihrintzis, G.A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 277. Springer, Singapore. https://doi.org/10.1007/978-981-19-1057-9_12
Download citation
DOI: https://doi.org/10.1007/978-981-19-1057-9_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1056-2
Online ISBN: 978-981-19-1057-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)