Top

Arabian Journal for Science and Engineering

Published in:

03-01-2021 | Research Article-Computer Engineering and Computer Science

Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Authors: Muzamil Hussan, Shabir A. Parah, Solihah Gull, G. J. Qureshi

Published in: Arabian Journal for Science and Engineering | Issue 4/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems.

previous article The Applications of Nature-Inspired Algorithms in Logistic Domains: A Comprehensive and Systematic Review

next article Numerical Study of the 3D Variable Coefficient Heat Transfer Problem by Using the Finite Pointset Method

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Luigi, A.; Antonio, I.; Giacomo, M.; Michele, N.: The Social Internet of Things (SIoT): When social networks meet the Internet of Things: Concept, architecture and network characterization. Comput. Netw. 56, 3594–3608 (2012). https://doi.org/10.1016/j.comnet.2012.07.010CrossRef

Pattar S., et al.: Progressive search algorithm for service discovery in an IoT ecosystem. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE (2019)

Hao, F.; Pei, Z.; Yang, L.T.: Diversified top-k maximal clique detection in Social Internet of Things. Future Gener. Comput. Syst. 107, 408–417 (2020)CrossRef

Imran, M.; Sohail, J.; Naveen, C.; Joel, J.P.C.R.: Enabling technologies for social internet of things. Future Gener. Comput. Syst. 715–717 (2019)

Roopa, M.S.; Santosh, P.; Rajkumar, B.; et al.: Social Internet of Things (SIoT): foundations, thrust areas, systematic review and future directions. Comput. Commun. 139, 32–57 (2019)CrossRef

He, H.; Chen, F.; Tai, H.M.; Kalker, T.; Zhang, J.: Performance analysis of a blockneighborhood-based selfrecovery fragile watermarking scheme. IEEE Trans. Inf. For. Secur. 7(1), 185–196 (2012)CrossRef

Islam, M.; Roy, A.; Laskar, R.H.: SVM-based robust image watermarking technique in LWT domain using different sub-bands. Neural Comput. Appl. 32, 1–25 (2020)

Korus, P.; Dziech, A.: Adaptive self-embedding scheme with controlled reconstruction performance. IEEE Trans. Inf. For. Secur. 9(2), 169–181 (2014)CrossRef

Kundur, D.; Hatzinakos, D.: Digital watermarking for telltale tamper proofing and authentication. Proc. IEEE 87(7), 1167–1180 (1999)CrossRef

10.

Lee, T.Y.; Lin, S.D.: Dual watermarking for image tamper detection and recovery. Pattern Recogn. 41, 3497–3506 (2008). https://doi.org/10.1016/j.patcog.2008.05.003CrossRefMATH

11.

Qi, X.; Xin, X.: A singular-value-based semi-fragile watermarking scheme for image content authentication with tamper localization. J. Vis. Commun. Image Represent. 30, 312–327 (2015)CrossRef

12.

Tai, W.L.; Liao, Z.J.: Image self-recovery with watermark self-embedding. Signal Process. Image Commun. 65, 11–25 (2018). https://doi.org/10.1016/j.image.2018.03.011CrossRef

13.

Wang, C.; Zhang, H.; Zhou, X.: A self-recovery fragile image watermarking with variable watermark capacity. Appl. Sci. 8, 548 (2018). https://doi.org/10.3390/appl8040548CrossRef

14.

He, H.J.; Zhang, J.S.; Chen, F.: Adjacent-block based statistical detection method for self-embedding watermarking techniques. Signal Process 89(8), 1557–1566 (2009)CrossRef

15.

Loan, N.A.; Hurrah, N.N.; Parah, S.A.; et al.: Secure and robust digital image watermarking using coefficient difference and chaotic encryption. IEEE Access 6, 19876–19897 (2018). https://doi.org/10.1109/ACCESS.2018.2808172CrossRef

16.

Azeroual, A.; Afdel, K.: Real-time imag localization based on fragile watermarking and faber-schauder wavelet. AEU Int. J. Electron. Commun. 79, 207–218 (2017). https://doi.org/10.1016/j.aeue.2017.06.001CrossRef

17.

Hurrah, N.N.; Parah, S.A.; Loan, N.A.; et al.: Dual watermarking framework for privacy protection and content authentication of multimedia. Future Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future02018.12.036CrossRef

18.

Gull, S.; Loan, N.A.; Parah, S.A.; et al.: An efficient watermarking technique for tamper detection and localization of medical images. J. Ambient Intell. Hum. Comput. 11, 1179–1808 (2018)

19.

Lin, P.L.; Hsieh, C.K.; Huang, P.W.: A hierarchical digital watermarking method for image tamper detection and recovery. Pattern Recogn. 38(12), 2519–2529 (2005). https://doi.org/10.1016/j.patcog.2005.02.007CrossRef

20.

Chang, Y.F.; Tai, W.L.A.: block-based watermarking scheme for image tamper detection and selfrecovery. Opto-Electron. Rev. 21(2), 182–190 (2013)MathSciNetCrossRef

21.

Lee, T.Y.; Lin, S.D.: Dual watermark for image tamper detection and recovery. Pattern Recogn. 41(11), 3497–3506 (2008). https://doi.org/10.1016/j.patcog.2008.05.003MathSciNetCrossRefMATH

22.

Singh, D.; Singh, S.K.: Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability. J. Vis. Commun. Image Represent. 38, 775–789 (2016)CrossRef

23.

Sarreshtedari, S.; Akhaee, M.A.: A source-channel coding approach to digital image protection and selfrecovery. IEEE Trans. Image Process. 24(7), 2266–2277 (2015)MathSciNetCrossRef

24.

Cao, F.; An, B.; Wang, J.; Ye, D.; Wang, H.: Hierarchical recovery for tampered images based on watermark self-embedding. Displays 46, 52–60 (2017)CrossRef

25.

Shehab, A.; Elhoseny, M.; Muhammad, K.; et al.: Secure and robust fragile watermarking scheme for medical images. IEEE Access 6, 10269–10278 (2018)CrossRef

26.

Zhang, Z.; Sun, H.; Gao, S.; Jin, S.: Self-recovery reversible image watermarking algorithm. PLoS One 13(6), e0199143 (2018)CrossRef

27.

Zhang, X.; Wang, S.: Fragile watermarking with error-free restoration capability. IEEE Trans. Multimedia 10(8), 1490–1499 (2008). https://doi.org/10.1109/TMM.2008.2007334CrossRef

28.

Li, K.F.; Chen, T.F.; Wu, S.C.: Image tamper detection and recovery system based on discrete wavelet transformation. In: Proceedings of IEEE Paci_c Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233),Victoria, BC, Canada, vol. 1, pp. 164–167 (2001). https://doi.org/10.1109/pacrim.2001.953548

29.

Tong, X.; Liu, Y.; Zhang, M.; Chen, Y.: A novel chaos-based fragile watermarking for image tampering detection and self-recovery. Sig. Process. Image Commun. 28(3), 301–308 (2013). https://doi.org/10.1016/j.image.2012.12.003CrossRef

30.

Naskar, R.; Chakraborty, R.S.: A generalized tamper localization approach for reversible watermarking algorithms. ACM Trans. Multimedia Comput. Commun. Appl. 9(3), 19:1–19:22 (2013)CrossRef

31.

Hsu, C.S.; Tu, S.F.: Image tamper detection and recovery using adaptive embedding rules. Measurement 88, 287–296 (2016). https://doi.org/10.1016/j.measurement.2016.03.053CrossRef

32.

Tiwari, A.; Sharma, M.; Tamrakar, R.K.: Watermarking based image authentication and tamper detection algorithm using vector quantization approach. AEU Int. J. Electron. Commun. 78, 114–123 (2017). https://doi.org/10.1016/j.aeue.2017.05.027CrossRef

33.

Li, Y.; Song, W.; et al.: A novel image tamper detection and self-recovery algorithm based on watermarking and chaotic system. Mathematics 7, 955 (2019). https://doi.org/10.3390/math7100955CrossRef

34.

Rajput, V.; Ansari, I.A.: Image tamper detection and self-recovery using multiple median watermarking. Multimedia Tools Appl (2019). https://doi.org/10.1007/s11042-019-07971-wCrossRef

35.

Parah, S.; Sheikh, J.; Loan, N.; Ahad, F.; Bhat, G.: Utilizing neighborhood coefficient correlation: a new image watermarking technique robust to singular and hybrid attacks. Multidimension. Syst. Signal Process. 29(3), 1095–1117 (2017)MathSciNetCrossRef

36.

Parah, S.; Sheikh, J.; Dey, N.; Bhat, G.M.: Realization of a new robust and secure watermarking technique using DC coefficient modification in pixel domain and chaotic encryption. J. Glob. Inf. Manag. 26(4), 80–102 (2017)CrossRef

37.

L. University, UCID Image Dataset, http://homepages.lboro.ac.uk/cogs/datasets/ucid/data/ucid.v2.tar.gz

Title: Tamper Detection and Self-Recovery of Medical Imagery for Smart Health
Authors: Muzamil Hussan
Shabir A. Parah
Solihah Gull
G. J. Qureshi
Publication date: 03-01-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 4/2021
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-05135-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 4/2021

An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization

Dynamic Frequency Scaling for Low-Power Operation of a Single-Core Processor: A Radial Basis Function Approach

Optimal Design of Transmission Shafts Using a Vortex Search Algorithm

Deep Learning for Discussion-Based Cross-Domain Performance Prediction of MOOC Learners Grouped by Language on FutureLearn

The Applications of Nature-Inspired Algorithms in Logistic Domains: A Comprehensive and Systematic Review

Design and Analysis of Pattern Matching Algorithms Based on QuRAM Processing

Premium Partners