Two-level information hiding for quantum images using optimal LSB
Tóm tắt
As a primary branch of information hiding, quantum image steganography has become one of the most popular areas of study in the field of security. In this paper, the two-level embedding approach for information hiding is surveyed using an optimal least significant bit (LSB) quantum steganography algorithm, which only modifies at most one qubit of the LSBs of each pixel to perform embedding. The first level is to hide the encrypted quantum secret image into a quantum watermark image, and the second level is to embed the quantum watermark image into a quantum cover image. Using the optimal LSB-based algorithm, the double embedding can make the position of embedding have a certain randomness, thus increasing security. In addition, the quantum secret image can be reconstructed by a series of inverse operations in the recovery phase; only the stego image and the key can extract the quantum secret image. The simulated experimental results and analysis demonstrate that the proposed scheme produces images of good visual quality, robustness and high security.
Tài liệu tham khảo
Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Inf. Process. 10, 63–84 (2011). https://doi.org/10.1007/s11128-010-0177-y
Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12, 2833–2860 (2013). https://doi.org/10.1007/s11128-013-0567-z
Li, H.-S., Zhu, Q., Zhou, R.-G., Li, M., Song, L., Ian, H.: Multidimensional color image storage, retrieval, and compression based on quantum amplitudes and phases. Inf. Sci. (NY) 273, 212–232 (2014). https://doi.org/10.1016/j.ins.2014.03.035
Jiang, N., Wang, L.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14, 1559–1571 (2015). https://doi.org/10.1007/s11128-014-0841-8
Zhou, R.-G., Hu, W., Luo, G., Liu, X., Fan, P.: Quantum realization of the nearest neighbor value interpolation method for INEQR. Quantum Inf. Process. 17, 166 (2018). https://doi.org/10.1007/s11128-018-1921-y
Zhou, R.-G., Hu, W., Fan, P., Ian, H.: Quantum realization of the bilinear interpolation method for NEQR. Sci. Rep. 7, 2511 (2017). https://doi.org/10.1038/s41598-017-02575-6
Fan, P., Zhou, R.-G., Hu, W.W., Jing, N.: Quantum image edge extraction based on Laplacian operator and zero-cross method. Quantum Inf. Process. 18, 27 (2019). https://doi.org/10.1007/s11128-018-2129-x
Fan, P., Zhou, R.-G., Hu, W.W., Jing, N.: Quantum image edge extraction based on classical Sobel operator for NEQR. Quantum Inf. Process. 18, 24 (2019). https://doi.org/10.1007/s11128-018-2129-x
Jiang, N., Dang, Y., Wang, J.: Quantum image matching. Quantum Inf. Process. 15, 3543–3572 (2016). https://doi.org/10.1007/s11128-016-1364-2
Luo, G., Zhou, R.-G., Liu, X., Hu, W., Luo, J.: Fuzzy matching based on gray-scale difference for quantum images. Int. J. Theor. Phys. 57, 2447–2460 (2018)
Yang, Y.-G., Zhao, Q.-Q., Sun, S.-J.: Novel quantum gray-scale image matching. Optik (Stuttg.) 126, 3340–3343 (2015). https://doi.org/10.1016/j.ijleo.2015.08.010
Jiang, N., Wu, W.Y., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13, 1223–1236 (2014). https://doi.org/10.1007/s11128-013-0721-7
Zhou, R.-G., Sun, Y., Fan, P.: Quantum image gray-code and bit-plane scrambling. Quantum Inf. Process. 14, 1717–1734 (2015). https://doi.org/10.1007/s11128-015-0964-6
Li, H.-S., Fan, P., Xia, H.-Y., Peng, H., Song, S.: Quantum implementation circuits of quantum signal representation and type conversion. IEEE Trans. Circuits Syst. I Regul. Pap. 99, 1–14 (2018). https://doi.org/10.1109/tcsi.2018.2853655
Pang, C.-Y., Zhou, R.-G., Hu, B.-Q., Hu, W.: Signal and image compression using quantum discrete cosine transform. Inf. Sci. (NY) 473, 121–141 (2019). https://doi.org/10.1016/j.ins.2018.08.067
Yan, F., Iliyasu, A.M., Le, P.Q.: Quantum image processing: a review of advances in its security technologies. Int. J. Quantum Inf. 15, 1730001 (2017). https://doi.org/10.1142/S0219749917300017
Jiang, N., Zhao, N., Wang, L.: LSB based quantum image steganography algorithm. Int. J. Theor. Phys. 55, 107–123 (2015). https://doi.org/10.1007/s10773-015-2640-0
Miyake, S., Nakamae, K.: A quantum watermarking scheme using simple and small-scale quantum circuits. Quantum Inf. Process. 15, 1849–1864 (2016). https://doi.org/10.1007/s11128-016-1260-9
Sang, J., Wang, S., Li, Q.: Least significant qubit algorithm for quantum images. Quantum Inf. Process. 15, 4441–4460 (2016). https://doi.org/10.1007/s11128-016-1411-z
Heidari, S., Pourarian, M.R., Gheibi, R., Naseri, M., Houshmand, M.: Quantum red–green–blue image steganography. Int. J. Quantum Inf. 15, 1750039 (2017). https://doi.org/10.1142/S0219749917500393
Zhang, T., Abd-el-atty, B., Amin, M., El-latif, A.A.A.: QISLSQb: a quantum image steganography scheme based on least significant qubit. In: International Conference on Mathematical, Computational and Statistical Sciences and Engineering, pp. 40–45 (2016)
Zhou, R.-G., Hu, W., Fan, P.: Quantum watermarking scheme through Arnold scrambling and LSB steganography. Quantum Inf. Process. 16, 212 (2017). https://doi.org/10.1007/s11128-017-1640-9
Li, P., Zhao, Y., Xiao, H., Cao, M.: An improved quantum watermarking scheme using small-scale quantum circuits and color scrambling. Quantum Inf. Process. 16, 127–160 (2017). https://doi.org/10.1007/s11128-017-1577-z
Naseri, M., Heidari, S., Baghfalaki, M., Fatahi, N., Gheibi, R., Farouk, A., Habibi, A.: A new secure quantum watermarking scheme. Optik (Stuttg.) 139, 77–86 (2017). https://doi.org/10.1016/j.ijleo.2017.03.091
Hu, W., Zhou, R.-G., Luo, J., Liu, B.: LSBs-based quantum color images watermarking algorithm in edge region. Quantum Inf. Process. 18, 16 (2019). https://doi.org/10.1007/s11128-018-2138-9
Zhou, R.-G., Hu, W., Luo, G., Fan, P., Ian, H.: Optimal LSBs-based quantum watermarking with lower distortion. Int. J. Quantum Inf. 16, 1850058 (2018). https://doi.org/10.1142/S0219749918500582
Luo, G., Zhou, R.-G., Luo, J., Hu, W., Zhou, Y., Ian, H.: Adaptive LSB quantum watermarking method using tri-way pixel value differencing. Quantum Inf. Process. 18, 49 (2019). https://doi.org/10.1007/s11128-018-2165-6
Luo, G., Zhou, R.-G., Hu, W., Luo, J., Liu, X., Ian, H.: Enhanced least significant qubit watermarking scheme for quantum images. Quantum Inf. Process. 17, 299 (2018). https://doi.org/10.1007/s11128-018-2075-7
El-latif, A.A.A., Abd-el-atty, B., Hossain, M.S.: Efficient quantum information hiding for remote medical image sharing. IEEE Access. 6, 21075–21083 (2018)
Tirkel, A.Z., Rankin, G.A., van Schyndel, R.G., Ho, W.J., Osborne, C.F.: Electronic watermark. In: Proceedings of Digital Image Computing: Techniques and Applications, pp. 666–672 (1993)
Gong, L.H., He, X.T., Cheng, S., Hua, T.X., Zhou, N.R.: Quantum image encryption algorithm based on quantum image XOR operations. Int. J. Theor. Phys. 55, 3234–3250 (2016). https://doi.org/10.1007/s10773-016-2954-6
Jiang, N., Wang, J., Mu, Y.: Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio. Quantum Inf. Process. 14, 4001–4026 (2015). https://doi.org/10.1007/s11128-015-1099-5