research-article

Image Encryption Based on Compressive Sensing and Scrambled Index for Secure Multimedia Transmission

Authors:

Hao QinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 12, Issue 4s

Article No.: 62, Pages 1 - 22

https://doi.org/10.1145/2903717

Published: 15 September 2016 Publication History

Abstract

With the rapid growth of multimedia message exchange and digital communication, multimedia big data has become a research hotspot in various fields. The storage and transmission of multimedia big data have high requirements for security. Images, covering the highest proportion of multimedia data, should be processed and transmitted with high security. Compressive sensing (CS) has a beneficial property for the encryption that the image can be recovered with fewer samples than conventional approaches use. In recent years, CS has been studied not only to reduce the resource requirements for signal acquisition but also to ensure the security of data. It is still an open challenge to improve security and enhance the quality of the decrypted image simultaneously using the key with small size. In this article, a CS-based encryption method is presented that associates the quantization with random measurement permutation. An enormous number of experiments have been conducted on both standard test images and face images chosen from the big database LFW. Experimental results show that our proposal has dramatic improvements on ensuring the security, enhancing the quality of the decrypted image, and raising the efficiency. Additionally, this proposal remarkably reduces storage and transmission resources. Accordingly, this encryption scheme can be applied to ensure the security of multimedia transmission.

References

[1]

Tiziano Bianchi, Valerio Bioglio, and Enrico Magli. 2014. On the security of random linear measurements. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’14). IEEE, 3992--3996.

[2]

Valerio Cambareri, Javier Haboba, Fabio Pareschi, H. Riccardo Rovatti, Gianluca Setti, and Kwok-wo Wong. 2013. A two-class information concealing system based on compressed sensing. In 2013 IEEE International Symposium on Circuits and Systems (ISCAS’13). IEEE, 1356--1359.

[3]

Valerio Cambareri, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti. 2015a. Low-complexity multiclass encryption by compressed sensing. IEEE Transactions on Signal Processing 63, 9 (2015), 2183--2195.

Digital Library

[4]

Valerio Cambareri, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti. 2015b. On known-plaintext attacks to a compressed sensing-based encryption: A quantitative analysis. IEEE Transactions on Information Forensics & Security 10, 10 (2015), 2182--2195.

Digital Library

[5]

Emmanuel Jean Candès and others. 2006. Compressive sampling. In Proceedings of the International Congress of Mathematicians, Vol. 3. 1433--1452.

[6]

Emmanuel Jean Candes, Justin Romberg, and Terence Tao. 2006. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59, 8 (2006), 1207--1223.

[7]

Emmanuel Jean Candes and Terence Tao. 2006. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory 52, 12 (2006), 5406--5425.

Digital Library

[8]

Emmanuel Jean Candès and Michael Wakin. 2008. An introduction to compressive sampling. IEEE Signal Processing Magazine 25, 2 (2008), 21--30.

[9]

David Leigh Donoho. 2006. Compressed sensing. Information Theory, 52, 4 (April 2006), 1289--1306.

Digital Library

[10]

Frederic Dufaux and Touradj Ebrahimi. 2008. Scrambling for privacy protection in video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology 18, 8 (2008), 1168--1174.

Digital Library

[11]

Lu Gan. 2007. Block compressed sensing of natural images. In 2007 15th International Conference on Digital Signal Processing. IEEE, 403--406.

[12]

Tiegang Gao and Zengqiang Chen. 2008. Image encryption based on a new total shuffling algorithm. Chaos, Solitons & Fractals 38, 1 (2008), 213--220.

[13]

Tom Goldstein and Stanley Osher. 2008. The split Bregman algorithm for LI regularized problems. UCLA CAM Report 08 29 (2008).

[14]

Adem Orsdemir, Oktay Altun, Gaurav Sharma, and Mark Bocko. 2008. On the security and robustness of encryption via compressed sensing. In IEEE Military Communications Conference, 2008 (MILCOM’08). IEEE, 1--7.

[15]

Stanley Osher, Martin Burger, Donald Goldfarb, Jinjun Xu, and Wotao Yin. 2005. An iterative regularization method for total variation-based image restoration. Multiscale Modeling & Simulation 4, 2 (2005), 460--489.

[16]

Robert Qiu and Michael Wicks. 2014. Cognitive Networked Sensing and Big Data. Springer.

Digital Library

[17]

Yaron Rachlin and Dror Baron. 2008. The secrecy of compressed sensing measurements. In 2008 46th Annual Allerton Conference on Communication, Control, and Computing. IEEE, 813--817.

[18]

Reena Shah and Bhavna Pancholi. 2014. Multimedia security techniques. Innovation Research in Electrical, Electronics, Instrumentation and Control Engineering 2, 5 (2014), 1477--1481.

[19]

Thomas Winkler and Bernhard Rinner. 2014. Security and privacy protection in visual sensor networks: A survey. ACM Computing Surveys (CSUR) 47, 1 (2014), 2.

Digital Library

[20]

Wotao Yin and Stanley Osher. 2013. Error forgetting of bregman iteration. Journal of Scientific Computing 54, 2--3 (2013), 684--695.

Digital Library

[21]

Wotao Yin, Stanley Osher, Donald Goldfarb, and Jerome Darbon. 2008. Bregman iterative algorithms for \ell_1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences 1, 1 (2008), 143--168.

Digital Library

[22]

Leo Yu Zhang, Kwok-Wo Wong, Chengqing Li, and Yushu Zhang. 2014a. Towards secure compressive sampling scheme. CoRR abs/1406.1725 (2014). http://arxiv.org/abs/1406.1725

[23]

Leo Yu Zhang, Kwok-Wo Wong, Yushu Zhang, and Qiuzhen Lin. 2014b. Joint quantization and diffusion for compressed sensing measurements of natural images. arXiv preprint arXiv:1411.6079 (2014).

[24]

Nanrun Zhou, Aidi Zhang, Fen Zheng, and Lihua Gong. 2014. Novel image compression--encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Optics & Laser Technology 62 (2014), 152--160.

Cited By

Cui WWang XFan XLiu SGao XZhao D(2024)Deep Network for Image Compressed Sensing Coding Using Local Structural SamplingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364944120:7(1-22)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649441
Huang HXiao DLiang J(2024)Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization ReconstructionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363530820:4(1-22)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3635308
He XLi LTong FPeng H(2024)Multilevel Privacy Protection for Social Media Based on 2-D Compressive SensingIEEE Internet of Things Journal10.1109/JIOT.2023.331381211:4(6878-6892)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3313812
Show More Cited By

Index Terms

Image Encryption Based on Compressive Sensing and Scrambled Index for Secure Multimedia Transmission
1. General and reference
  1. Cross-computing tools and techniques
    1. Design
2. Security and privacy
  1. Formal methods and theory of security
    1. Formal security models
  2. Network security
    1. Mobile and wireless security

Recommendations

A visually secure image encryption scheme based on compressive sensing

A novel visually secure image encryption scheme based on compressive sensing (CS) is proposed. Firstly, the plain image is transformed into wavelet coefficients, and then confused by a zigzag path and encrypted into a compressed cipher image using ...
An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications

A novel image coding scheme under parallel compressive sensing framework is proposed.The scheme achieves resistance to chosen plaintext attack by a counter mode operation.The scheme overcomes the defect of energy information leakage in compressive ...
Compressive Sensing-Based Image Encryption and Authentication in Edge-Clouds
MultiMedia Modeling
Abstract
Compressive sensing (CS) is often utilized to encrypt data on resource constrained terminals due to its lightweight and confidentiality. However, due to its low security level, it cannot meet the security requirements when interacting with the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 12, Issue 4s

Special Section on Trust Management for Multimedia Big Data and Special Section on Best Papers of ACM Multimedia 2015

November 2016

242 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2997658

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2016

Accepted: 01 February 2016

Revised: 01 January 2016

Received: 01 October 2015

Published in TOMM Volume 12, Issue 4s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Natural Science Foundation of China
Research Fund for the Doctoral Program of Higher Education of China
111 Project
Fundamental Research Funds for the Central Universities
ISN State Key Laboratory

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
380
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Cui WWang XFan XLiu SGao XZhao D(2024)Deep Network for Image Compressed Sensing Coding Using Local Structural SamplingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364944120:7(1-22)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649441
Huang HXiao DLiang J(2024)Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization ReconstructionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363530820:4(1-22)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3635308
He XLi LTong FPeng H(2024)Multilevel Privacy Protection for Social Media Based on 2-D Compressive SensingIEEE Internet of Things Journal10.1109/JIOT.2023.331381211:4(6878-6892)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3313812
Deb SDas AKar N(2023)An Applied Image Cryptosystem on Moore’s Automaton Operating on δ (q)/𝔽2ACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361443320:2(1-20)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3614433
Koc BArnavut ZKoçak H(2023)Concurrent encryption and lossless compression using inversion ranksJournal of Information Security and Applications10.1016/j.jisa.2023.10358778:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.jisa.2023.103587
Singh KSingh A(2022)Towards Integrating Image Encryption with Compression: A SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/349834218:3(1-21)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3498342
Liang JXiao DXiang YDoss R(2022)A Compressed Sensing Based Image Compression-Encryption Coding Scheme without Auxiliary Information TransmissionICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839131(5573-5578)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9839131
Xiao JLiang WCai JZhu HLi XXie S(2022)An Investigation of Blockchain-Based ShardingSmart Computing and Communication10.1007/978-3-031-28124-2_66(695-704)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-28124-2_66
Zeng XHu WLiu XChen YShao WSun L(2022)Few-Shot Learning for Medical Numerical Understanding Based on Machine Reading ComprehensionSmart Computing and Communication10.1007/978-3-031-28124-2_58(617-628)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-28124-2_58
Zhao HTan JDuan J(2022)Research on Action Recognition Based on Zero-shot LearningSmart Computing and Communication10.1007/978-3-031-28124-2_17(175-187)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-28124-2_17
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents