Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Reservoir computing based encryption-then-compression scheme of image achieving lossless compression

Published: 05 December 2024 Publication History

Abstract

Image encryption-then-compression (ETC), combining encryption and compression techniques, is a powerful strategy for image privacy protection. One of the most significant concerns in ETC is to realize a trade-off between high compression and high-quality recovery. To this end, we propose a novel ETC scheme based on a ring-network optical dynamic system and a reservoir computing (RC) system. This optical dynamics system is newly constructed by using three QD spin-VCSELs, then the optical RC system is further specifically designed. To ensure security, we develop a secret key generation method, which involves jointly injecting the original image and chaotic data into the RC system for chaotic transformation to produce high-security secret keys. In our scheme, the original image is first encrypted through scrambling and diffusion using the generated keys, then the obtained encrypted image is segmented into multiple sub-blocks, followed by using uniform down-sampling to select data, the unsampled data will be discarded and the sampled data is to be retained as the resulting compressed image. Flexible compression ratio can be achieved by varying sub-block sizes, with larger sub-blocks yielding higher compression. To realize high-quality recovery, the relationship between the compressed image and the discarded data is constructed in a learning manner by means of the designed RC system, which is conducive to the reconstruction process, resulting in lossless recovery. This work can effectively overcome the limitations of existing schemes in terms of simultaneously achieving both well-performing compression and lossless reconstruction. Experimental results are given which demonstrate the high security and superior performance of the proposed scheme.

Highlights

A new ring-network optical dynamic system is constructed.
A novel optical reservoir computing system is presented.
A novel secret key generation method is introduced.
A novel encryption-then-compression scheme is proposed.

References

[1]
Adams M.J., Alexandropoulos D., Analysis of quantum-dot spin-VCSELs, Ieee Photonics Journal 4 (4) (2012) 1124–1132.
[2]
Ahmad I., Shin S., A novel hybrid image encryption–compression scheme by combining chaos theory and number theory, Signal Processing: Image Communication 98 (2021).
[3]
Alexandropoulos D., Alseyab R., Henning I., Adams M., Instabilities in quantum-dot spin-VCSELs, Optics Lettersc 37 (10) (2012) 1700–1702.
[4]
Alharthi S., Orchard J., Clarke E., Henning I., Adams M., 1300 Nm optically pumped quantum dot spin vertical external-cavity surface-emitting laser, Applied Physics Letters 107 (15) (2015).
[5]
Alvarez G., Li S., Some basic cryptographic requirements for chaos-based cryptosystems, International Journal of Bifurcation and Chaos 16 (08) (2006) 2129–2151.
[6]
Appeltant L., Soriano M.C., Van der Sande G., Danckaert J., Massar S., Dambre J., et al., Information processing using a single dynamical node as complex system, Nature Communication 2 (1) (2011) 468.
[7]
Bezerra J.I.M., Machado G., Molter A., Soares R.I., Camargo V., A novel simultaneous permutation–diffusion image encryption scheme based on a discrete space map, Chaos, Solitons & Fractals 168 (2023).
[8]
Bhattacharya P., Basu D., Das A., Saha D., Quantum dot polarized light sources, Semiconductor Science and Technology 26 (1) (2010).
[9]
Bimberg D., Kirstaedter N., Ledentsov N., Alferov Z.I., Kop’Ev P., Ustinov V., InGaAs-GaAs quantum-dot lasers, Ieee Journal of Selected Topics in Quantum Electronics 3 (2) (1997) 196–205.
[10]
Chuman T., Sirichotedumrong W., Kiya H., Encryption-then-compression systems using grayscale-based image encryption for JPEG images, IEEE Transactions on Information Forensics and Security 14 (6) (2018) 1515–1525.
[11]
Deng Y., Li Y., A 2D hyperchaotic discrete memristive map and application in reservoir computing, Ieee Transactions on Circuits and Systems II-express Briefs 69 (3) (2022) 1817–1821.
[12]
Gerhardt N., Li M., Jähme H., Höpfner H., Ackemann T., Hofmann M., Ultrafast spin-induced polarization oscillations with tunable lifetime in vertical-cavity surface-emitting lasers, Applied Physics Letters 99 (15) (2011).
[13]
Höpfner H., Lindemann M., Gerhardt N.C., Hofmann M.R., Controlled switching of ultrafast circular polarization oscillations in spin-polarized vertical-cavity surface-emitting lasers, Applied Physics Letters 104 (2) (2014).
[14]
Jiang D., Tsafack N., Boulila W., Ahmad J., Barba-Franco J., ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption, Expert Systems with Applications 236 (2024).
[15]
Jiang X., Xie Y., Liu B., Ye Y., Song T., Chai J., et al., Dynamics of mutually coupled quantum dot spin-VCSELs subject to key parameters, Nonlinear Dynamics 105 (4) (2021) 3659–3671.
[16]
Kumar M., Vaish A., An efficient encryption-then-compression technique for encrypted images using SVD, Digital Signal Processing 60 (2017) 81–89.
[17]
Lee J., Falls W., Oszwałdowski R., Žutić I., Spin modulation in semiconductor lasers, Applied Physics Letters 97 (4) (2010).
[18]
Lee O., Wei T., Stenning K.D., Gartside J.C., Prestwood D., Seki S., et al., Task-adaptive physical reservoir computing, Nature Materials 23 (1) (2024) 79–87.
[19]
Li Z., Liu F., Yang W., Peng S., Zhou J., A survey of convolutional neural networks: Analysis, applications, and prospects, IEEE Transactions on Neural Networks and Learning Systems 33 (12) (2022) 6999–7019.
[20]
Li N., Susanto H., Cemlyn B., Henning I., Adams M., Mapping bifurcation structure and parameter dependence in quantum dot spin-VCSELs, Optics Express 26 (11) (2018) 14636–14649.
[21]
Lindemann M., Pusch T., Michalzik R., Gerhardt N.C., Hofmann M.R., Frequency tuning of polarization oscillations: Toward high-speed spin-lasers, Applied Physics Letters 108 (4) (2016).
[22]
Liu X., Tong X., Zhang M., Wang Z., Fan Y., Image compression and encryption algorithm based on uniform non-degeneracy chaotic system and fractal coding, Nonlinear Dynamics 111 (9) (2023) 8771–8798.
[23]
Liu W., Zeng W., Dong L., Yao Q., Efficient compression of encrypted grayscale images, IEEE Transactions on Image Processing 19 (4) (2010) 1097–1102.
[24]
Lugnan A., Katumba A., Laporte F., Freiberger M., Sackesyn S., Ma C., et al., Photonic neuromorphic information processing and reservoir computing, APL Photonics 5 (2) (2020).
[25]
Luo Y., Liang Y., Zhang S., Liu J., Wang F., An image encryption scheme based on block compressed sensing and Chen’s system, Nonlinear Dynamics 111 (7) (2023) 6791–6811.
[26]
Nan S., Feng X., Wu Y., Zhang H., Remote sensing image compression and encryption based on block compressive sensing and 2D-LCCCM, Nonlinear Dynamics 108 (3) (2022) 2705–2729.
[27]
Nishioka D., Tsuchiya T., Namiki W., Takayanagi M., Imura M., Koide Y., et al., Edge-of-chaos learning achieved by ion-electron–coupled dynamics in an ion-gating reservoir, Science Advances 8 (50) (2022) eade1156.
[28]
Puteaux P., Puech W., An efficient MSB prediction-based method for high-capacity reversible data hiding in encrypted images, IEEE Transactions on Information Forensics and Security 13 (7) (2018) 1670–1681.
[29]
Qin C., Zhou Q., Cao F., Dong J., Zhang X., Flexible lossy compression for selective encrypted image with image inpainting, IEEE Transactions on Circuits and Systems for Video Technology 29 (11) (2019) 3341–3355.
[30]
Robb J., Chen Y., Timmons A., Hall K., Shchekin O., Deppe D., Time-resolved Faraday rotation measurements of spin relaxation in InGaAs/GaAs quantum dots: Role of excess energy, Applied Physics Letters 90 (15) (2007).
[31]
Sun X., Chen Z., Wang L., He C., A lossless image compression and encryption algorithm combining JPEG-LS, neural network and hyperchaotic system, Nonlinear Dynamics 111 (16) (2023) 15445–15475.
[32]
Wang C., Feng Y., Li T., Xie H., Kwon G.-R., A new encryption-then-compression scheme on gray images using the Markov random field, Computers, Materials & Continua 56 (1) (2018) 107–121.
[33]
Wang C., Hu J., Bian S., Ni J., Zhang X., A customized deep network based encryption-then-lossy-compression scheme of color images achieving arbitrary compression ratios, IEEE Transactions on Circuits and Systems for Video Technology 33 (8) (2023) 4322–4336.
[34]
Wang J., Ji Z., Zhang H., Wang Z., Meng Q., Synchronization of generally uncertain Markovian inertial neural networks with random connection weight strengths and image encryption application, IEEE Transactions on Neural Networks and Learning Systems 34 (9) (2023) 5911–5925.
[35]
Wang C., Ni J., Huang Q., A new encryption-then-compression algorithm using the rate–distortion optimization, Signal Processing: Image Communication 39 (2015) 141–150.
[36]
Wang C., Ni J., Zhang X., Huang Q., Efficient compression of encrypted binary images using the Markov random field, IEEE Transactions on Information Forensics and Security 13 (5) (2018) 1271–1285.
[37]
Wang C., Song L., An image encryption scheme based on chaotic system and compressed sensing for multiple application scenarios, Information Sciences 642 (2023).
[38]
Wang C., Zhang T., Chen H., Huang Q., Ni J., Zhang X., A novel encryption-then-lossy-compression scheme of color images using customized residual dense spatial network, IEEE Transactions on Multimedia 25 (2022) 4026–4040.
[39]
Wang J., Zhang M., Tong X., Wang Z., An image compression encryption scheme based on chaos and SPECK-DCT hybrid coding, Nonlinear Dynamics 112 (11) (2024) 9581–9602.
[40]
Wang M., Zhou N., Li L., Xu M., A novel image encryption scheme based on chaotic apertured fractional Mellin transform and its filter bank, Expert Systems with Applications 207 (2022).
[41]
Wu X., Shi H., Jie M., Duan S., Wang L., A novel image compression and encryption scheme based on conservative chaotic system and DNA method, Chaos, Solitons & Fractals 172 (2023).
[42]
Xu Q., Sun K., He S., Zhu C., An effective image encryption algorithm based on compressive sensing and 2D-SLIM, Optics and Lasers in Engineering 134 (2020).
[43]
Yang Y., Cheng M., Ding Y., Zhang W., A visually meaningful image encryption scheme based on lossless compression SPIHT coding, IEEE Transactions on Services Computing 16 (4) (2023) 2387–2401.
[44]
Yang J., Feng X.-f., Teng L., Liu H., Zhang H., A lossless compression and encryption scheme for sequence images based on 2D-CTCCM, MDFSM and STP, Nonlinear Dynamics 112 (8) (2024) 6715–6741.
[45]
Yildirim M., Oguz I., Kaufmann F., Escalé M.R., Grange R., Psaltis D., et al., Nonlinear optical feature generator for machine learning, APL Photonics 8 (10) (2023).
[46]
Zhang H., Nan S.x., Liu Z.h., Yang J., Feng X.f., Lossless and lossy remote sensing image encryption-compression algorithm based on DeepLabv3+ and 2D CS, Applied Soft Computing 159 (2024).
[47]
Zhang H., Wang X., Sun Y., Wang X., A novel method for lossless image compression and encryption based on LWT, SPIHT and cellular automata, Signal Processing: Image Communication 84 (2020).
[48]
Zhou J., Au O.C., Zhai G., Tang Y.Y., Liu X., Scalable compression of stream cipher encrypted images through context-adaptive sampling, IEEE Transactions on Information Forensics and Security 9 (11) (2014) 1857–1868.
[49]
Zhou J., Liu X., Au O.C., Tang Y.Y., Designing an efficient image encryption-then-compression system via prediction error clustering and random permutation, IEEE Transactions on Information Forensics and Security 9 (1) (2014) 39–50.
[50]
Zhu L., Jiang D., Ni J., Wang X., Rong X., Ahmad M., et al., A stable meaningful image encryption scheme using the newly-designed 2D discrete fractional-order chaotic map and Bayesian compressive sensing, Signal Processing 195 (2022).
[51]
Zhu H., Zou J., Zhang H., Shi Y., Luo S., Wang N., et al., Space-efficient optical computing with an integrated chip diffractive neural network, Nature Communication 13 (1) (2022) 1044.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal  Volume 256, Issue C
Dec 2024
1582 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 05 December 2024

Author Tags

  1. Image encryption-then-compression
  2. Optical dynamics system
  3. Reservoir computing
  4. Lossless compression

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 12 Feb 2025

Other Metrics

Citations

View Options

View options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media