With the extensive use of cloud services in different applications, it's a problem for the cloud service provider to manage or process the privacy data that are encrypted by the content owner. Therefore, signal processing technology in the encrypted domain has attracted the attention of researchers. In this paper, we propose a new reversible data hiding method for encrypted images based on two-phase histogram shifting. In the proposed method, the original image is encrypted by using special image division and additive homomorphic encryption. After image encryption, the encrypted image can partially maintain spatial correlation for data embedding while the content security of the encrypted image is ensured. Due to the spatial correlation, the data hider can generate two difference histograms from the encrypted image, which provide high embedding capacity. A two-phase histogram shift scheme is used to embed the secret data into the two difference histograms. At the receiver side, the secret data can be extracted from the encrypted image or the decrypted image, and the image can be recovered to its original version without any error. The experimental results demonstrated that the proposed method can efficiently improve the capacity of data embedding and outperform other related methods, while the visual quality of the marked image can be maintained.
Citation: Kaimeng Chen, Chin-Chen Chang. High-capacity reversible data hiding in encrypted images based on two-phase histogram shifting[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 3947-3964. doi: 10.3934/mbe.2019195
[1] | Fangyuan Chen, Rong Yuan . Dynamic behavior of swine influenza transmission during the breed-slaughter process. Mathematical Biosciences and Engineering, 2020, 17(5): 5849-5863. doi: 10.3934/mbe.2020312 |
[2] | Aurelie Akossi, Gerardo Chowell-Puente, Alexandra Smirnova . Numerical study of discretization algorithms for stable estimation of disease parameters and epidemic forecasting. Mathematical Biosciences and Engineering, 2019, 16(5): 3674-3693. doi: 10.3934/mbe.2019182 |
[3] | Kasia A. Pawelek, Anne Oeldorf-Hirsch, Libin Rong . Modeling the impact of twitter on influenza epidemics. Mathematical Biosciences and Engineering, 2014, 11(6): 1337-1356. doi: 10.3934/mbe.2014.11.1337 |
[4] | Meili Li, Ruijun Zhai, Junling Ma . The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada. Mathematical Biosciences and Engineering, 2023, 20(8): 13849-13863. doi: 10.3934/mbe.2023616 |
[5] | Karen R. Ríos-Soto, Baojun Song, Carlos Castillo-Chavez . Epidemic spread of influenza viruses: The impact of transient populations on disease dynamics. Mathematical Biosciences and Engineering, 2011, 8(1): 199-222. doi: 10.3934/mbe.2011.8.199 |
[6] | Rodolfo Acuňa-Soto, Luis Castaňeda-Davila, Gerardo Chowell . A perspective on the 2009 A/H1N1 influenza pandemic in Mexico. Mathematical Biosciences and Engineering, 2011, 8(1): 223-238. doi: 10.3934/mbe.2011.8.223 |
[7] | Qingling Zeng, Kamran Khan, Jianhong Wu, Huaiping Zhu . The utility of preemptive mass influenza vaccination in controlling a SARS outbreak during flu season. Mathematical Biosciences and Engineering, 2007, 4(4): 739-754. doi: 10.3934/mbe.2007.4.739 |
[8] | Akhil Kumar Srivastav, Pankaj Kumar Tiwari, Prashant K Srivastava, Mini Ghosh, Yun Kang . A mathematical model for the impacts of face mask, hospitalization and quarantine on the dynamics of COVID-19 in India: deterministic vs. stochastic. Mathematical Biosciences and Engineering, 2021, 18(1): 182-213. doi: 10.3934/mbe.2021010 |
[9] | Hiroshi Nishiura . Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 49-64. doi: 10.3934/mbe.2011.8.49 |
[10] | Oren Barnea, Rami Yaari, Guy Katriel, Lewi Stone . Modelling seasonal influenza in Israel. Mathematical Biosciences and Engineering, 2011, 8(2): 561-573. doi: 10.3934/mbe.2011.8.561 |
With the extensive use of cloud services in different applications, it's a problem for the cloud service provider to manage or process the privacy data that are encrypted by the content owner. Therefore, signal processing technology in the encrypted domain has attracted the attention of researchers. In this paper, we propose a new reversible data hiding method for encrypted images based on two-phase histogram shifting. In the proposed method, the original image is encrypted by using special image division and additive homomorphic encryption. After image encryption, the encrypted image can partially maintain spatial correlation for data embedding while the content security of the encrypted image is ensured. Due to the spatial correlation, the data hider can generate two difference histograms from the encrypted image, which provide high embedding capacity. A two-phase histogram shift scheme is used to embed the secret data into the two difference histograms. At the receiver side, the secret data can be extracted from the encrypted image or the decrypted image, and the image can be recovered to its original version without any error. The experimental results demonstrated that the proposed method can efficiently improve the capacity of data embedding and outperform other related methods, while the visual quality of the marked image can be maintained.
[1] | C. Qin, X. Chen, X. Luo, et al., Perceptual image hashing via dual-cross pattern encoding and salient structure detection, Inform. Sci., 423 (2018), 284–302. |
[2] | C. Qin, C. C. Chang and Y. P. Chiu, A novel joint data-hiding and compression scheme based on SMVQ and image inpainting, IEEE Trans. Image Process., 23 (2014), 969–978. |
[3] | C. Qin, P. Ji, X. Zhang, et al., Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy, Signal Process., 138 (2017), 280–293. |
[4] | Z. Qian, H. Xu, X. Luo, et al., New framework of reversible data hiding in encrypted JPEG bitstreams, IEEE Trans. Circuits Syst. Video Technol., 29 (2018), 351–362. |
[5] | J. Tian, Reversible data embedding using a difference expansion, IEEE Trans. Circuits Syst. Video Technol., 13 (2003), 890–896. |
[6] | Y. Hu, H. K. Lee and J. Li, DE-based reversible data hiding with improved overflow location map, IEEE Trans. Circuits Syst. Video Technol., 19 (2009), 250–260. |
[7] | Y. Qiu, Z. Qian and L. Yu, Adaptive reversible data hiding by extending the generalized integer transformation, IEEE Signal Process. Lett., 23 (2016), 130–134. |
[8] | Z. Ni, Y. Q. Shi, N. Ansari, et al., Reversible data hiding, IEEE Trans. Circuits Syst. Video Technol., 16 (2006), 354–362. |
[9] | T. S. Nguyen, C. C. Chang and N. T. Huynh, A novel reversible data hiding scheme based on difference-histogram modification and optimal EMD algorithm, J. Vis. Commun. Image Represent., 33 (2015), 389–397. |
[10] | J. Wang, J. Ni, X. Zhang, et al., Rate and distortion optimization for reversible data hiding using multiple histogram shifting, IEEE Trans. Cybern., 47 (2016), 315–326. |
[11] | X. Li, J. Li, B. Li, et al., High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion, Signal Process., 93 (2013), 198–205. |
[12] | X. Qu and H. J. Kim, Pixel-based pixel value ordering predictor for high-fidelity reversible data hiding, Signal Process., 111 (2015), 249–260. |
[13] | B. Ou, X. Li and J. Wang, High-fidelity reversible data hiding based on pixel-value-ordering and pairwise prediction-error expansion, J. Vis. Commun. Image Represent., 39 (2016), 12–23. |
[14] | X. Zhang, Reversible data hiding in encrypted images, IEEE Signal Process. Lett., 18 (2011), 255–258. |
[15] | W. Hong, T. Chen and H. Wu, An improved reversible data hiding in encrypted images using side match, IEEE Signal Process. Lett., 19 (2012), 199–202. |
[16] | X. Liao and C. Shu, Reversible data hiding in encrypted images based on absolute mean difference of multiple neighboring pixels, J. Vis. Commun. Image Represent., 28 (2015), 21–27. |
[17] | C. Qin and X. Zhang, Effective reversible data hiding in encrypted image with privacy protection for image content, J. Vis. Commun. Image Represent., 31 (2015), 154–164. |
[18] | X. Wu and W. Sun, High-capacity reversible data hiding in encrypted images by prediction error, Signal Process., 104 (2014), 387–400. |
[19] | X. Zhang, Separable reversible data hiding in encrypted image, IEEE Trans. Inf. Forensics Security, 7 (2012), 826–832. |
[20] | C. Qin, W. Zhang, F. Cao, et al., Separable reversible data hiding in encrypted images via adaptive embedding strategy with block selection, Signal Process., 153 (2018), 109–122. |
[21] | C. Qin, Z. He, X. Luo and J. Dong, Reversible data hiding in encrypted image with separable capability and high embedding capacity, Inform. Sci., 465 (2018), 285–304. |
[22] | X. Zhang, Z. Qian, G. Feng, et al., Efficient reversible data hiding in encrypted images, J. Vis. Commun. Image Represent., 25 (2014), 322–328. |
[23] | Z. Qian and X. Zhang, Reversible data hiding in encrypted image with distributed source encoding, IEEE Trans. Circuits Syst. Video Technol., 26 (2016), 636–646. |
[24] | M. Li, D. Xiao, Y. Zhang, et al., Reversible data hiding in encrypted images using cross division and additive homomorphism, Signal Process.: Image Commun., 39 (2015), 234–248. |
[25] | D. Xiao, Y. Xiang, H. Zheng, et al., Separable reversible data hiding in encrypted image based on pixel value ordering and additive homomorphism, J. Vis. Commun. Image Represent., 45 (2017), 1–10. |
[26] | S. Yi, Y. Zhou and Z. Hua, Reversible data hiding in encrypted images using adaptive block-level prediction-error expansion, Signal Process.: Image Commun., 64 (2018), 78–88. |
[27] | R. L. Rivest, L. Adleman and M. L. Dertouzos, On data banks and privacy homomorphisms, Found. Secure Comput., 4 (1978), 169–180. |
1. | Md. Samsuzzoha, Manmohan Singh, David Lucy, A numerical study on an influenza epidemic model with vaccination and diffusion, 2012, 219, 00963003, 122, 10.1016/j.amc.2012.04.089 | |
2. | S. Dorjee, Z. Poljak, C. W. Revie, J. Bridgland, B. McNab, E. Leger, J. Sanchez, A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations, 2013, 60, 18631959, 383, 10.1111/zph.12010 | |
3. | Jon Brugger, Christian L. Althaus, Transmission of and susceptibility to seasonal influenza in Switzerland from 2003 to 2015, 2020, 30, 17554365, 100373, 10.1016/j.epidem.2019.100373 | |
4. | Hiroshi Nishiura, Ping Yan, Candace K. Sleeman, Charles J. Mode, Estimating the transmission potential of supercritical processes based on the final size distribution of minor outbreaks, 2012, 294, 00225193, 48, 10.1016/j.jtbi.2011.10.039 | |
5. | Fred Brauer, Carlos Castillo-Chavez, Zhilan Feng, 2019, Chapter 9, 978-1-4939-9826-5, 311, 10.1007/978-1-4939-9828-9_9 | |
6. | Rodolfo Acuna-Soto, 2009, Chapter 9, 978-90-481-2312-4, 189, 10.1007/978-90-481-2313-1_9 | |
7. | Parameter estimation and uncertainty quantification for an epidemic model, 2012, 9, 1551-0018, 553, 10.3934/mbe.2012.9.553 | |
8. | Y. Yang, J. D. Sugimoto, M. E. Halloran, N. E. Basta, D. L. Chao, L. Matrajt, G. Potter, E. Kenah, I. M. Longini, The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus, 2009, 326, 0036-8075, 729, 10.1126/science.1177373 | |
9. | Gerardo Chowell, Fred Brauer, 2009, Chapter 1, 978-90-481-2312-4, 1, 10.1007/978-90-481-2313-1_1 | |
10. | Chaeshin Chu, Junehawk Lee, Dong Hoon Choi, Seung-Ki Youn, Jong-Koo Lee, Sensitivity Analysis of the Parameters of Korea’s Pandemic Influenza Preparedness Plan, 2011, 2, 22109099, 210, 10.1016/j.phrp.2011.11.048 | |
11. | Tridip Sardar, Soumalya Mukhopadhyay, Amiya Ranjan Bhowmick, Joydev Chattopadhyay, Alessandro Vespignani, An Optimal Cost Effectiveness Study on Zimbabwe Cholera Seasonal Data from 2008–2011, 2013, 8, 1932-6203, e81231, 10.1371/journal.pone.0081231 | |
12. | Lisa Sattenspiel, Regional patterns of mortality during the 1918 influenza pandemic in Newfoundland, 2011, 29, 0264410X, B33, 10.1016/j.vaccine.2011.02.046 | |
13. | Ian York, Ruben O. Donis, 2012, Chapter 221, 978-3-642-36870-7, 241, 10.1007/82_2012_221 | |
14. | Charlotte Jackson, Emilia Vynnycky, Punam Mangtani, The Relationship Between School Holidays and Transmission of Influenza in England and Wales, 2016, 184, 0002-9262, 644, 10.1093/aje/kww083 | |
15. | Gerardo Chowell, Hiroshi Nishiura, Quantifying the transmission potential of pandemic influenza, 2008, 5, 15710645, 50, 10.1016/j.plrev.2007.12.001 | |
16. | Maciej F Boni, Bui Huu Manh, Pham Quang Thai, Jeremy Farrar, Tran Tinh Hien, Nguyen Tran Hien, Nguyen Van Kinh, Peter Horby, Modelling the progression of pandemic influenza A (H1N1) in Vietnam and the opportunities for reassortment with other influenza viruses, 2009, 7, 1741-7015, 10.1186/1741-7015-7-43 | |
17. | Nadhem Selmi, A model of the 2014 Ebola virus: Evidence of West Africa, 2019, 010, 10.29328/journal.ijcv.1001004 | |
18. | Leonardo López, Germán Burguerner, Leonardo Giovanini, Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach, 2014, 7, 1756-0500, 10.1186/1756-0500-7-234 | |
19. | I. De Falco, A. Della Cioppa, U. Scafuri, E. Tarantino, 2021, 9780128245361, 75, 10.1016/B978-0-12-824536-1.00005-8 | |
20. | Imelda Trejo, Nicolas W. Hengartner, Alberto d’Onofrio, A modified Susceptible-Infected-Recovered model for observed under-reported incidence data, 2022, 17, 1932-6203, e0263047, 10.1371/journal.pone.0263047 | |
21. | Ella Ziegler, Katarina L. Matthes, Peter W. Middelkamp, Verena J. Schuenemann, Christian L. Althaus, Frank Rühli, Kaspar Staub, Retrospective modelling of the disease and mortality burden of the 1918–1920 influenza pandemic in Zurich, Switzerland, 2025, 50, 17554365, 100813, 10.1016/j.epidem.2025.100813 |