The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
An image compression and encryption algorithm based on chaotic system and compressive sensing
Abstract
Abstract For a linear image encryption system, it is vulnerable to the chosen-plaintext attack. To overcome the weakness and reduce the correlation among pixels of the encryption image, an effective image compression and encryption algorithm based on chaotic system and compressive sensing is proposed. The original image is first permuted by the Arnold transform to reduce the block effect in the compression process, and then the resulting image is compressed and re-encrypted by compressive sensing, simultaneously. Moreover, the bitwise XOR operation based on chaotic system is performed on the measurements to change the pixel values and a pixel scrambling method is employed to disturb the positions of pixels. Besides, the keys used in chaotic systems are related to the plaintext image. Simulation results verify the effectiveness and reliability of the proposed image compression and encryption algorithm with considerable compression and security performance.
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Citations
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Symmetric Ciphers Based on Two-Dimensional Chaotic Maps
Fridrich, J. (1998) · International Journal of Bifurcation and Chaos · doi:10.1142/s021812749800098x · Cited by 1,917
(2011) · Cited by 904
Local Shannon entropy measure with statistical tests for image randomness
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2D Logistic-Sine-coupling map for image encryption
Hua, Z., Jin, F., Xu, B., & Huang, H. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.03.010 · Cited by 595
An image encryption approach based on chaotic maps
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Medical image encryption using high-speed scrambling and pixel adaptive diffusion
Hua, Z., Yi, S., & Zhou, Y. (2018) · Signal Processing · doi:10.1016/j.sigpro.2017.10.004 · Cited by 350
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Self-citationA visually secure image encryption scheme based on compressive sensing
Chai, X., Gan, Z., Chen, Y., & Zhang, Y. (2017) · Signal Processing · doi:10.1016/j.sigpro.2016.11.016 · Cited by 313
Cited by
Yu, S.-S., Zhou, N.-R., Gong, L.-H., & Nie, Z. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.105816 · Cited by 183
Self-citationImage encryption based on a combination of multiple chaotic maps
Elkandoz, M. T., & Alexan, W. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12595-8 · Cited by 172
Alexan, W., Alexan, N., & Gabr, M. (2023) · Fractal and Fractional · doi:10.3390/fractalfract7040287 · Cited by 144
Qian, X., Yang, Q., Li, Q., Liu, Q., Wu, Y., & Wang, W. (2021) · IEEE Access · doi:10.1109/access.2021.3073514 · Cited by 90
Gabr, M., Korayem, Y., Chen, Y.-L., Yee, P. L., Ku, C. S., & Alexan, W. (2023) · IEEE Access · doi:10.1109/access.2023.3326848 · Cited by 79
Metadata
- Authors
- Gong, L., Qiu, K., Deng, C., & Zhou, N.
- Venue
- Optics & Laser Technology
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 115
- Pages
- 257-267
- ISSN
- 0030-3992 (ISSN-L)
- OpenAlex
- W2915863155
- Cited by
- 256
- References
- 34
- FWCI
- 16.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gong, L., Qiu, K., Deng, C., & Zhou, N. (2019). An image compression and encryption algorithm based on chaotic system and compressive sensing. Optics & Laser Technology. https://doi.org/10.1016/j.optlastec.2019.01.039
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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