Abstract
Secure image coding schemes have attracted much attention in the information field. However, most of the secure image transmission schemes suffer from poor rate-distortion (R-D) performance. In this paper, the property of the measurement matrix and the correlation among the compressive sensing (CS) measurements are utilized to develop a secure image data transmission scheme with optimized rate-distortion. At the encoder side, block CS is applied to capture an image with the DCT matrix. Next, the measurements need to be divided into three parts and then quantized with different bit-depths, where the residual coefficients are quantized with fewer bits. Lastly, all bits will be scrambled into an image and then it will be diffused with the forward-reverse diffusion. At the decoder side, the image will be reconstructed with the corresponding decryption and decoding algorithm. Compared with the adopted benchmarks and some existing works, the proposed scheme can reach a higher level of R-D performance. Moreover, the simulation analyses prove that the proposed cryptoscheme has a good performance in terms of security.
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References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials 17(4), 2347–2376 (2015)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)
Zhou, N., Zhang, A., Zheng, F., Gong, L.: Novel image compression-encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Optics Laser Technol. 62, 152–160 (2014)
Zhou, N., Li, H., Wang, D., Pan, S., Zhou, Z.: Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform. Optics Commun. 343, 10–21 (2015)
Zhou, N., Pan, S., Cheng, S., Zhou, Z.: Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing. Optics Laser Technol. 82, 121–133 (2016)
Li, L., Wen, G., Wang, Z., Yang, Y.: Efficient and secure image communication system based on compressed sensing for IoT monitoring applications. IEEE Trans. Multimedia 22(1), 82–95 (2019)
Hu, G., Xiao, D., Wang, Y., Xiang, T.: An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J. Vis. Commun. Image Represent. 44, 116–127 (2017)
Luo, Y., et al.: A robust image encryption algorithm based on Chua’s circuit and compressive sensing. Signal Process. 161, 227–247 (2019)
Zhu, L., Song, H., Zhang, X., Yan, M., Zhang, L., Yan, T.: A novel image encryption scheme based on nonuniform sampling in block compressive sensing. IEEE Access 7, 22161–22174 (2019)
Niu, Z., Zheng, M., Zhang, Y., Wang, T.: A new asymmetrical encryption algorithm based on semitensor compressed sensing in WBANs. IEEE Internet Things J. 7(1), 734–750 (2019)
Li, L., Liu, L., Peng, H., Yang, Y., Cheng, S.: Flexible and secure data transmission system based on semitensor compressive sensing in wireless body area networks. IEEE Internet Things J. 6(2), 3212–3227 (2018)
Zhang, Y., Xiang, Y., Zhang, L.Y., Yang, L.X., Zhou, J.: Efficiently and securely outsourcing compressed sensing reconstruction to a cloud. Inf. Sci. 496, 150–160 (2019)
Zhang, Y., He, Q., Chen, G., Zhang, X., Xiang, Y.: A low-overhead, confidentiality-assured, and authenticated data acquisition framework for IoT. IEEE Trans. Industr. Inf. 16(12), 7566–7578 (2020)
Wang, L., Wu, X., Shi, G.: Binned progressive quantization for compressive sensing. IEEE Trans. Image Process. 21(6), 2980–2990 (2012)
Chen, Z., Hou, X., Qian, X., Gong, C.: Efficient and robust image coding and transmission based on scrambled block compressive sensing. IEEE Trans. Multimedia 20(7), 1610–1621 (2017)
Chen, Z., et al.: Compressive sensing multi-layer residual coefficients for image coding. IEEE Trans. Circuits Syst. Video Technol. 30(4), 1109–1120 (2019)
Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: 2009 16th IEEE international conference on image processing (ICIP), pp. 3021–3024. IEEE, Cairo (2009)
Candes, E.J., Plan, Y.: A probabilistic and RIPless theory of compressed sensing. IEEE Trans. Inf. Theory 57(11), 7235–7254 (2011)
Alvarez, G., Li, S.: Some basic cryptographic requirements for chaos-based cryptosystems. Int. J. Bifurcat. Chaos 16(08), 2129–2151 (2006)
Acknowledge
The work was supported by the National Key R&D Program of China (Grant no. 2020YFB1805401), the National Natural Science Foundation of China (Grant No. 62072063) and the Project Supported by Graduate Student Research and Innovation Foundation of Chongqing, China (Grant No. CYB 21062).
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Xiao, D., Lan, S. (2021). Secure Image Coding Based on Compressive Sensing with Optimized Rate-Distortion. In: Gao, D., Li, Q., Guan, X., Liao, X. (eds) Information and Communications Security. ICICS 2021. Lecture Notes in Computer Science(), vol 12919. Springer, Cham. https://doi.org/10.1007/978-3-030-88052-1_8
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DOI: https://doi.org/10.1007/978-3-030-88052-1_8
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