data hiding thesis

Vision Research Lab

Publications, multimedia data hiding: from fundamental issues to practical techniques.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Journal Proposal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Quantum image steganography schemes for data hiding: a survey.

1. Introduction

2. background.

• Hiding capacity—Evaluates the size of data that the scheme is capable to embed into the carrier image. The sizes can vary from hiding text segments to images equal to the size of the cover image.
• Imperceptibility—demonstrates the opaqueness of the hidden image and assesses whether the human eye can depict a steganographic image.
• Security—determines the difficulty for unauthorized parties to access and manipulate the hidden data.
• Robustness—exhibits how robust the hidden data is against received attacks and its ability to maintain its state.
• Unambiguity—the ability to identify the copyright owner of the secret data.

3. Quantum Image Steganography Schemes

3.1. least significant qubit, 3.2. turtle shell-based matrix, 3.3. inverted pattern approach, 3.4. pixel value differencing, 3.5. quantum noisy channels effect, 3.5.1. amplitude damping, 3.5.2. phase damping channel, 3.5.3. bit flip channel, 3.5.4. depolarizing channel, 3.6. entanglement as a degree of freedom, 3.7. quantum image scrambling, 3.8. double-layer matrix coding, 3.9. interpolation-based scheme, 3.10. controlled alternate quantum walks protocol, 3.11. quantum emd protocol, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Taleby Ahvanooey, M.; Li, Q.; Hou, J.; Rajput, A.R.; Chen, Y. Modern text hiding, text steganalysis, and applications: A comparative analysis. Entropy 2019 , 21 , 355. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Sutherland, C.; Brun, T.A. Quantum steganography over noiseless channels: Achievability and bounds. Phys. Rev. A 2020 , 101 , 052319. [ Google Scholar ] [ CrossRef ]
• Jiang, N.; Dang, Y.; Wang, J. Quantum image matching. Quantum Inf. Process. 2016 , 15 , 3543–3572. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhou, R.G.; Luo, J.; Liu, X.; Zhu, C.; Wei, L.; Zhang, X. A novel quantum image steganography scheme based on LSB. Int. J. Theor. Phys. 2018 , 57 , 1848–1863. [ Google Scholar ] [ CrossRef ]
• Şahin, E.; Yilmaz, İ. A novel quantum steganography algorithm based on LSBq for multi-wavelength quantum images. Quantum Inf. Process. 2018 , 17 , 1–24. [ Google Scholar ] [ CrossRef ]
• Tudorache, A.G.; Manta, V.; Caraiman, S. Quantum Steganography Using Two Hidden Thresholds. Adv. Electr. Comput. Eng. 2021 , 21 , 79–88. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Lu, K.; Gao, Y.; Wang, M. NEQR: A novel enhanced quantum representation of digital images. Quantum Inf. Process. 2013 , 12 , 2833–2860. [ Google Scholar ] [ CrossRef ]
• Şahin, E.; Yilmaz, I. QRMW: Quantum representation of multi wavelength images. Turk. J. Electr. Eng. Comput. Sci. 2018 , 26 , 768–779. [ Google Scholar ] [ CrossRef ]
• Luo, G.; Zhou, R.G.; Hu, W. Efficient quantum steganography scheme using inverted pattern approach. Quantum Inf. Process. 2019 , 18 , 1–24. [ Google Scholar ] [ CrossRef ]
• Abd-El-Atty, B.; Iliyasu, A.M.; Alaskar, H.; El-Latif, A.; Ahmed, A. A robust quasi-quantum walks-based steganography protocol for secure transmission of images on cloud-based E-healthcare platforms. Sensors 2020 , 20 , 3108. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Luo, J.; Zhou, R.G.; Luo, G.; Li, Y.; Liu, G. Traceable quantum steganography scheme based on pixel value differencing. Sci. Rep. 2019 , 9 , 1–12. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Zhao, S.; Yan, F.; Chen, K.; Yang, H. Interpolation-based high capacity quantum image steganography. Int. J. Theor. Phys. 2021 , 60 , 3722–3743. [ Google Scholar ] [ CrossRef ]
• Nagy, M.; Nagy, N. Quantum Steganography by Harnessing Entanglement as a Degree of Freedom. IEEE Access 2020 , 8 , 213671–213681. [ Google Scholar ] [ CrossRef ]
• Sharma, V.K.; Sharma, P.C.; Goud, H.; Singh, A. Hilbert quantum image scrambling and graph signal processing-based image steganography. Multimed. Tools Appl. 2022 , 81 , 17817–17830. [ Google Scholar ] [ CrossRef ]
• Wang, M.X.; Yang, H.M.; Jiang, D.H.; Yan, B.; Pan, J.S.; Liu, T. A novel quantum color image steganography algorithm based on turtle shell and LSB. Quantum Inf. Process. 2022 , 21 , 1–32. [ Google Scholar ] [ CrossRef ]
• Sun, H.; Qu, Z.; Sun, L.; Chen, X.; Xu, G. High-efficiency quantum image steganography protocol based on double-layer matrix coding. Quantum Inf. Process. 2022 , 21 , 1–27. [ Google Scholar ] [ CrossRef ]
• Qu, Z.; Sun, H.; Zheng, M. An efficient quantum image steganography protocol based on improved EMD algorithm. Quantum Inf. Process. 2021 , 20 , 1–29. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Wang, S. Efficient steganographic embedding by exploiting modification direction. IEEE Commun. Lett. 2006 , 10 , 781–783. [ Google Scholar ] [ CrossRef ]
• Qu, Z.; Wu, S.; Sun, L.; Wang, M.; Wang, X. Effects of quantum noises on χ state-based quantum steganography protocol. Math. Biosci. Eng. 2019 , 16 , 4999–5021. [ Google Scholar ] [ CrossRef ]
• Tilly, J.; Chen, H.; Cao, S.; Picozzi, D.; Setia, K.; Li, Y.; Grant, E.; Wossnig, L.; Rungger, I.; Booth, G.H.; et al. The variational quantum eigensolver: A review of methods and best practices. arXiv 2021 , arXiv:2111.05176. [ Google Scholar ] [ CrossRef ]
• Al-Shareeda, M.A.; Anbar, M.; Manickam, S.; Khalil, A.; Hasbullah, I.H. Security and Privacy Schemes in Vehicular Ad-Hoc Network With Identity-Based Cryptography Approach: A Survey. IEEE Access 2021 , 9 , 121522–121531. [ Google Scholar ] [ CrossRef ]
• Herodotus; Holland, T.; Cartledge, P. The Histories ; Viking: New York, NY, USA, 2014. [ Google Scholar ]
• Sang, J.; Wang, S.; Li, Q. A novel quantum representation of color digital images. Quantum Inf. Process. 2017 , 16 , 1–14. [ Google Scholar ] [ CrossRef ]
• Chang, C.C.; Liu, Y.; Nguyen, T.S. A novel turtle shell based scheme for data hiding. In Proceedings of the 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kitakyushu, Japan, 27–29 August 2014; pp. 89–93. [ Google Scholar ]
• Jiang, N.; Zhao, N.; Wang, L. LSB based quantum image steganography algorithm. Int. J. Theor. Phys. 2016 , 55 , 107–123. [ Google Scholar ] [ CrossRef ]
• Subramanian, N.; Elharrouss, O.; Al-Maadeed, S.; Bouridane, A. Image steganography: A review of the recent advances. IEEE Access 2021 , 9 , 23409–23423. [ Google Scholar ] [ CrossRef ]
• Su, J.; Guo, X.; Liu, C.; Li, L. A new trend of quantum image representations. IEEE Access 2020 , 8 , 214520–214537. [ Google Scholar ] [ CrossRef ]
• Sun, B.; Iliyasu, A.; Yan, F.; Dong, F.; Hirota, K. An RGB multi-channel representation for images on quantum computers. J. Adv. Comput. Intell. Intell. Inf. 2013 , 17 , 404–417. [ Google Scholar ] [ CrossRef ]
• Wang, L.; Ran, Q.; Ma, J.; Yu, S.; Tan, L. QRCI: A new quantum representation model of color digital images. Opt. Commun. 2019 , 438 , 147–158. [ Google Scholar ] [ CrossRef ]
• Yang, C.H. Inverted pattern approach to improve image quality of information hiding by LSB substitution. Pattern Recognit. 2008 , 41 , 2674–2683. [ Google Scholar ] [ CrossRef ]
• Hussain, M.; Wahab, A.W.A.; Anuar, N.B.; Salleh, R.; Noor, R.M. Pixel value differencing steganography techniques: Analysis and open challenge. In Proceedings of the 2015 IEEE International Conference on Consumer Electronics-Taiwan, Taipei, Taiwan, 6–8 June 2015; pp. 21–22. [ Google Scholar ]
• Wu, D.C.; Tsai, W.H. A steganographic method for images by pixel-value differencing. Pattern Recognit. Lett. 2003 , 24 , 1613–1626. [ Google Scholar ] [ CrossRef ]
• Shaw, B.A.; Brun, T.A. Quantum steganography with noisy quantum channels. Phys. Rev. A 2011 , 83 , 022310. [ Google Scholar ] [ CrossRef ]
• Gupta, S.; Goyal, A.; Bhushan, B. Information hiding using least significant bit steganography and cryptography. Int. J. Mod. Educ. Comput. Sci. 2012 , 4 , 27. [ Google Scholar ] [ CrossRef ]
• Heidari, S.; Vafaei, M.; Houshmand, M.; Tabatabaey-Mashadi, N. A dual quantum image scrambling method. Quantum Inf. Process. 2019 , 18 , 1–23. [ Google Scholar ] [ CrossRef ]
• Crandall, R. Some notes on steganography. Posted Steganography Mail. List 1998 , 1998 , 1–6. [ Google Scholar ]
• Qu, Z.; Cheng, Z.; Wang, X. Matrix coding-based quantum image steganography algorithm. IEEE Access 2019 , 7 , 35684–35698. [ Google Scholar ] [ CrossRef ]
• Zhang, W.; Zhang, X.; Wang, S. Maximizing steganographic embedding efficiency by combining Hamming codes and wet paper codes. In Proceedings of the International Workshop on Information Hiding, Santa Barbara, CA, USA, 19–21 May 2008; pp. 60–71. [ Google Scholar ]
• Malik, A.; Wang, H.; Chen, T.; Yang, T.; Khan, A.N.; Wu, H.; Chen, Y.; Hu, Y. Reversible data hiding in homomorphically encrypted image using interpolation technique. J. Inf. Secur. Appl. 2019 , 48 , 102374. [ Google Scholar ] [ CrossRef ]
• Jung, K.H. A survey of interpolation-based reversible data hiding methods. Multimed. Tools Appl. 2018 , 77 , 7795–7810. [ Google Scholar ] [ CrossRef ]
• Zhou, W. Review on quantum walk algorithm. In Journal of Physics: Conference Series ; IOP Publishing: Bristol, UK, 2021; Volume 1748, p. 032022. [ Google Scholar ]
• Qu, Z.; Cheng, Z.; Liu, W.; Wang, X. A novel quantum image steganography algorithm based on exploiting modification direction. Multimed. Tools Appl. 2019 , 78 , 7981–8001. [ Google Scholar ] [ CrossRef ]
• Khandelwal, J.; Sharma, V.K.; Raguru, J.K.; Goyal, H. Recent Trend of Transform Domain Image Steganography Technique for Secret Sharing. In Cyber Warfare, Security and Space Research ; Joshi, S., Bairwa, A.K., Nandal, A., Radenkovic, M., Avsar, C., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 171–185. [ Google Scholar ]
• Singh, S.; Siddiqui, T.J. Transform domain techniques for image steganography. In Computer Vision: Concepts, Methodologies, Tools, and Applications ; IGI Global: Hershey, PA, USA, 2018; pp. 170–186. [ Google Scholar ]
• Hashim, M.; Rahim, M.; Alwan, A. A review and open issues of multifarious image steganography techniques in spatial domain. J. Theor. Appl. Inf. Technol. 2018 , 96 , 956–977. [ Google Scholar ]

Click here to enlarge figure

Share and Cite

Min-Allah, N.; Nagy, N.; Aljabri, M.; Alkharraa, M.; Alqahtani, M.; Alghamdi, D.; Sabri, R.; Alshaikh, R. Quantum Image Steganography Schemes for Data Hiding: A Survey. Appl. Sci. 2022 , 12 , 10294. https://doi.org/10.3390/app122010294

Min-Allah N, Nagy N, Aljabri M, Alkharraa M, Alqahtani M, Alghamdi D, Sabri R, Alshaikh R. Quantum Image Steganography Schemes for Data Hiding: A Survey. Applied Sciences . 2022; 12(20):10294. https://doi.org/10.3390/app122010294

Min-Allah, Nasro, Naya Nagy, Malak Aljabri, Mariam Alkharraa, Mashael Alqahtani, Dana Alghamdi, Razan Sabri, and Rana Alshaikh. 2022. "Quantum Image Steganography Schemes for Data Hiding: A Survey" Applied Sciences 12, no. 20: 10294. https://doi.org/10.3390/app122010294

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Advertisement

Applications of data hiding techniques in medical and healthcare systems: a survey

• Review Article
• Published: 30 April 2018
• Volume 7 , article number  6 , ( 2018 )

Cite this article

• Hedieh Sajedi 1  

1002 Accesses

15 Citations

Explore all metrics

Nowadays, scientists make effort to provide security for the communication channels. Several security and privacy threats are introduced via the Internet as the major communication channel. Therefore, information exchanging through the Internet should be protected and secured. Now healthcare systems are getting very common in the world. A health care system should preserve privacy while sending patients’ information, prevent the patients’ information from tampering, and prevent any sabotage in the healthcare systems. Therefore, it is crucial to provide security in such systems to attract the confidence of people for using the Internet (network)-based health care systems. In this respect, the goal of this paper is to review the accessories to provide security in healthcare systems. Our objective is to state the achievements of a literature review regarding the security of health care systems provided by information hiding methods including cryptography, steganography and watermarking methods. Furthermore, discussion about these methods, the media employed in these methods such as medical images and biomedical signals, their pros and cons and the applications in healthcare systems are provided. Correspondingly, we share the visions into the open research problems and highlight future directions in this extent.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Bio-signal data sharing security through watermarking: a technical survey

A Recent Survey on Information-Hiding Techniques

An efficient reversible and secure patient data hiding algorithm for E-healthcare

Explore related subjects.

• Artificial Intelligence

Abuturab M (2013) Color information security system using Arnold transform and double structured phase encoding in gyrator transform domain. Opt Laser Technol 4:525–532

Article   Google Scholar  

Ahmad T (2014) Shared secret-based steganography for protecting medical data. In: Proceedings of international conference on computer, control, informatics and its applications (IC3INA), pp 87–92

Ahmed SM, Al-Zoubi Q, Abo-Zahhad M (2007) A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform. J Med Eng Technol 31(1):54–61

Ahson SA, Ilyas M (2010) Cloud computing and software services theory and techniques. CRC Press, Boca Raton

Book   Google Scholar  

Alattar AM (2004) Reversible watermark using the difference expansion of a generalized integer transform. IEEE Trans Image Processing 13:1147–1156

Article   MathSciNet   Google Scholar  

AzamiSidek K, Mai V, Khalil I (2014) Data mining in mobile ECG based biometric identification. J Netw Comput Appl 44:83–91

Bacharova L, Bang L, Szathmary V, Mateasik A (2014) Imaging QRS complex and ST segment in myocardial infarction. J Electrocardiol 47(4):438–447

Boucsein W (2012) Electrodermal activity. Springer, Berlin, p 2 (ISBN 978-1-461-41126-0)

Bulling A et al (2009) Wearable EOG goggles: seamless sensing and context-awareness in everyday environments. J Ambient Intell Smart Environ 1(2):157–171

Google Scholar  

Calvillo J, Roman I, Roa LM (2013) Empowering citizens with access control mechanisms to their personal health resources. Int J Med Inf 82:58–72

Cavagnino D, Lucenteforte M, Grangetto M (2015) High capacity reversible data hiding and content protection for radiographic images. Sig Process 117:258–269

Celik MU, Sharma G, Tekalp AM, Saber E (2005) Lossless generalized-LSB data embedding. IEEE Trans Image Process 14:253–266

Chakraborty S, Samanta S, Biswas D, Dey N, Chaudhuri S (2013) Particle swarm optimization based parameter optimization technique in medical information hiding. In: Proceedings of IEEE international conference on computational intelligence and computing research

Chao HM, Hsu CM, Miaou SG (2002) A data-hiding technique with authentication, integration, and confidentiality for electronic patient records. IEEE Trans Inf Technol Biomed 6:46–53

Chaoab H, Twua S, Hsu C (2005) A patient-identity security mechanism for electronic medical records during transit and at rest. Med Inf Internet Med 30(3):227–240

Coatrieux G, Maitre H, Sankur B (2001) Strict integrity control of biomedical images. In: Proceedings of SPIE security and watermarking of multimedia

Correa MAgustinaG, Leber EL (2011) Noise removal from EEG signals in polisomnographic records applying adaptive filters in cascade, adaptive filtering applications, Chap. 8. InTech, Croatia

Cox IJ, Miller ML, Bloom JA, Fridrich J (2008) Ton Kalker digital watermarking and steganography. Morgan Kaufmann, Burlington

Crichtona JHM (2009) Defining high, medium, and low security in forensic mental healthcare: the development of the Matrix of Security in Scotland. J Forensic Psychiatry Psychol 20(3):333–353

Dao T, Pouletaut P, Charleux F, Lazáry Á, Eltes P, Varga P, Ho Ba Tho M (2015) Multimodal medical imaging (CT and dynamic MRI) data and computer-graphics multi-physical model for the estimation of patient specific lumbar spine muscle forces. Data Knowl Eng 96–97:3–18

De la Rosa Algarin A, Demurjian S, Berhe S, Pavlich-Mariscal J (2012) A security framework for xml schemas and documents for healthcare. In: Proceedings of IEEE international conference on bioinformatics and biomedicine workshops, pp 782–789

Delbarpour S, Sajedi H (2017) Image steganography with artificial immune system. In: 7th joint conference on artificial intelligence and robotics and the 9th RoboCup IranOpen International Symposium

Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21

Edward Jero S, Ramu P, Ramakrishnan S (2014) Discrete wavelet transform and singular value decomposition based ECG steganography for secured patient information transmission. J Med Syst 38(10):132–140

Eisenberg RL, Margulis AR (2011) A patient’s guide to medical imaging. Oxford University Press, Oxford (ISBN 978-0-19-972991-3)

Electroencephalogram (EEG) (2018). http://hopkinsmedicine.org/healthlibrary/test_procedures/neurological/electroencephalogram_eeg_92,p07655/ . Accessed 4 Aug 2018

Engin M, Cidam O, Engin EZ (2005) Wavelet transformation based watermarking technique for human electrocardiogram (ECG). J Med Syst 29(6):589–594

Fernandez-Aleman JL, Carrion Seor I, angel Oliver Lozoya P, Toval A (2013) Security and privacy in electronic health records: a systematic literature review. J Biomed Inform 46:541–562

Gao MZ, Wu ZG, Wang L (2013) Comprehensive evaluation for HE based contrast enhancement techniques. Adv Intell Syst Appl 2:331–338

Gavrovska A, Bogdanović V, Reljin I, Reljin B (2014) Automatic heart sound detection in pediatric patients without electrocardiogram reference via pseudo-affine Wigner–Ville distribution and Haar wavelet lifting. Comput Methods Programs Biomed 113(2):515–528

Ghazvini A, Shukur Z (2013) Security challenges, and success factors of electronic healthcare system. In: Proceedings of 4th international conference on electrical engineering and informatics (ICEEI 2013), pp 212–219

Giakoumaki A, Pavlopoulos S, Koutsouris D (2006) Multiple image watermarking applied to health information management. IEEE Trans Inf Technol Biomed 10:722–732

Giannetti V (2003) Medical errors: the hidden victim. Clin Res Regul Affairs 20(4):425–432

Gkoulalas-Divanis A, Loukides G, Sun J (2014) Publishing data from electronic health records while preserving privacy: a survey of algorithms. J Biomed Inform 50:4–19

Goljan M, Fridrich J, Du R (2001) Distortion-free data embedding for images. In: Proceedings of 4th information hiding workshop, pp 27–41

Golpira H, Danyali H (2010) Reversible blind watermarking for medical images based on wavelet histogram shifting. In: Proceedings of IEEE international symposium on signal processing and information technology (ISSPIT), pp 31–36

Gritzalis S, Iliadis J, Gritzalis D, Spinellis D, Katsikas S (1999) Developing secure web-based medical applications. Med Inf Internet Med 24(1):75–90

Gritzalisa D, Katsikasa S, Keklikoglou J, Tomaras A (1991) Data security in medical information systems: technical aspects of a proposed legislation. Med Inf 16(4):371–383

Guo X, Zhuang T (2003) A lossless watermarking scheme for enhancing security of medical data in PACS. In: Proceedings of SPIE and medical imaging, pp 350–359

Guo X, Zhuang T (2009) A region-based lossless watermarking scheme for enhancing security of medical data. J Digit Imaging 22(1):53–64

Hafizah Hassan N, Ismail Z (2012) A conceptual model for investigating factors influencing information security culture in healthcare environment. International Congress on Interdisciplinary Business and Social Science

Hu F, Jiang M, Wagner M, Dong D (2007) Privacy-preserving telecardiology sensor networks: toward a low-cost portable wireless hardware/software code sign. IEEE Trans Inf Technol Biomed 11(6):619–627

Ibaida A, Khalil I, Al-Shammary D (2010) Embedding patients confidential data in ECG signal for healthcare information systems. In: Proceedings of IEEE engineering in medicine and biology society

Ibrahim W, Saniee M, Abadeh (2017) Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition. J Theor Biol 421:1–15

Ibrahim W, Saniee M, Abadeh (2018) Protein fold recognition using deep kernelized extreme learning machine and linear discriminant analysis. Neural Comput Appl 1–14. https://doi.org/10.1007/s00521-018-3346-z

Istepanian RS, Petrosian AA (2000) Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system. IEEE Trans Inf Technol Biomed 4(3):200–211

Joseph T, Remya UL (2014) ECG steganography based privacy protection of medical data for telemedicine application. J Dent Med Sci 13(8):85–94

Kamal S, Khan M (2014a) An integrated algorithm for local sequence alignment. Netw Model Anal Health Inf Bioinf 3:68

Kamal Md, Khan M (2014b) Chapman–Kolmogorov equations for global PPIs with discriminant-EM. Int J Biomath 07(05):1450053

Article   MATH   Google Scholar  

Kamal MdS, Khan M, Dev K, Chowdhury L, Dey N (2016) An optimized graph-based metagenomic gene classification approach: metagenomic gene analysis, classification and clustering in biomedical signal processing, IEG Global Publisher, pp 290–314

Kamal MdS, Chowdhury L, Khan M, Ashour AS, Tavares J, Dey N (2017a) Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images, Comput Biol Chem 68:231–244

Kamal MdS, Sarowar MdG, Dey N, Ashour AS, Ripon SH, Panigrahi BK, Tavares JRS (2017b) Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification. Int J Mach Learn Cybern:1–24

Kamal MdS, Dey N, Ashour AS (2017c) Large scale medical data mining for accurate diagnosis: a blueprint, handbook of large-scale distributed computing in smart healthcare. Scalable computing and communications. Springer, Cham

Kamal MdS, Dey N, Nimmy S, Ripon S, Ali N, Ashour A, Karaa W, Nguyen G, Shi F (2018) Evolutionary framework for coding area selection from cancer data. Neural Comput Appl 29(4):1015–1037

Kaur S, Singhal R, Farooq O, Ahuja B (2010) Digital watermarking of ECG data for secure wireless communication. In: Proceedings of international conference on recent trends in information, telecommunication and computing, pp 140–144

Khan M, Kamal MS (2015) Performance evaluation of Warshall algorithm and dynamic programming for Markov chain i0 local sequence alignment. Interdiscip Sci 7(1):78–81

Khokhar RH, Chen R, Fung BCM, Man Lui S (2014) Quantifying the costs and benefits of privacy-preserving health data publishing. J Biomed Inform 50:107–121

Kullback S (1987) Letter to the editor: the Kullback–Leibler distance. Am Stat 41(4):340–341

Li M, Yu S, Zheng Y, Ren K, Lou W (2013) Scalable and secure sharing of personal health records in cloud computing using attribute based encryption. IEEE Trans Parallel Distrib Syst 24(1):131–143

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JPA et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 62(10):e1–e34

Lin JC (1999) Applying telecommunication technology to health care delivery. IEEE Eng Med Biol Mag 18(4):28–31

Malashree KS, Jagadish KN, Suma M (2014) Confidential data hiding using wavelet based ECG steganography. Int J Eng Res Appl 4(5):84–88

Mazurczyk W, Szczypiorski K, Lubacz J (2013) 4 new ways to smuggle messages across the internet. IEEE Spectr 50(11):42–4523

Moody G, Mark R, Goldberger A (1988) Evaluation of the ‘TRIM’ ECG data compressor. In: Proceedings of computers in cardiology, pp 167–70

Mu-Hsing A, Kuo (2011) Opportunities and challenges of cloud computing to improve health care services. J Med Internet Res 13(3):e67

Naheed T, Usman I, Khan TM, Dar AH, Shafique M (2014) Intelligent reversible watermarking technique in medical images using GA and PSO. Optik 125:2515–2525

Nambakhsh MS, Ahmadian A, Ghavami M, Dilmaghani RS, Karimi-Fard S (2006) A novel blind watermarking of ECG signals on medical images using EZW algorithm. In: Proceedings of IEEE engineering in medicine and biology society, pp 3274–3277

National Biomedical Imaging Archive. http://imaging.nci.nih.gov . Accessed 28 Apr 2018

Nimmy S, Kamal MdS, Hossain MI, Dey N, Ashour AS, Shi F (2017) Neural skyline filtering for imbalance features classification. Int J Comput Intell Appl 16(03):1–25

Omotosho A, Adegbola O, Olayemi Mikail O, Emuoyibofarhe J (2014) A secure electronic prescription system using steganography with encryption key implementation. Int J Comput Inf Technol 3(5):980–986

Pangalos GJ (1995) Design, and implementation of secure medical database systems. Med Inform 20(3):265–277

Pangalosa GJ (1993) Medical database security evaluation. Med Inform 18(4):283–292

Phukpattaranont P (2015) QRS detection algorithm based on the quadratic filter. Expert Syst Appl 42(11):4867–4877

Poremba S (2015) Cyber security is growing importance for medical devices too. Forbes magazine. 1/19/2015

Rao NV, Kumarib VM (2011) Watermarking in medical imaging for security and authentication. Inf Secur J 20(3):148–155

Rubio OJ, Alesanco A, Garcia J (2013) Secure information embedding into 1D biomedical signals based on SPIHT. J Biomed Inform 46:653–664

Said A, Pearlman W (1996) A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circ Syst Video Technol 6(3):243–250

Sajedi H, Jamzad M (2009a) Adaptive batch steganography considering image embedding capacity. Opt Eng 48(8):1–10

Sajedi H, Jamzad M (2009b) Secure steganography based on embedding capacity. J Inf Secur 8(6):433–445

Sajedi H, Jamzad M (2010a) HYSA: hybrid steganographic approach using multiple steganography methods. Secur Commun Netw 4(10):1173–1184

Sajedi H, Jamzad M (2010b) BSS: boosted steganography scheme with cover image preprocessing. Expert Syst Appl 37:7703–7710

Sajedi H, Mohammadipanah F, Shariat Panahi HK (2018) An image analysis-aided method for redundancy reduction in differentiation of identical actinobacterial strains. Future Microbiol 13(3):313–329

Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Proces 41(12):3445–3462

Shoukat IA, Bakar KA, Iftikhar M (2011) A survey about the latest trends and research issues of cryptographic elements. Int J Comput Sci Issues 8(3):140–149

Srinivasan Y, Nutter B, Mitra S, Phillips B, Ferris D (2004) Secure transmission of medical records using high capacity steganography. In: Proceedings of 17th IEEE symposium on computer-based medical systems

Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509

Stansfield S (2005) Structuring information and incentives to improve health. Bull World Health Organ 83(8):562

Stokes M, Blythe M (2001) Muscle Sounds in physiology, sports science and clinical investigation. Medintel, Oxford (ISBN 0-9540572-0-1)

Subhedara MS, Mankar VH (2014) Current status and key issues in image steganography: a survey. Comput Sci Rev 13(14):95–113

Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans Circ Syst Video Technol 13:890–896

Traver V, Monton E, Bayo JL, Garcia JM, Hernandez J, Guillen S (2003) Multiagent home telecare platform for patients with cardiac diseases. In: Proceedings of computing in cardiology, pp 117–120

Tupakula U, Varadharajan V (2013) security techniques for counteracting attacks in mobile healthcare services. In: Proceedings of 3rd international conference on current and future trends of information and communication technologies in healthcare, pp 374–381

Tzelepi S, Pangalos G, Nikolacopoulou G (2002) Security of medical multimedia, medical informatics and the internet in medicine. Taylor & Francis, London, vol 27, no 3, pp 169–184

Ullsberger P, Delorme A (2007) EEGLAB study set. http://tinyurl.com/bsdkyaj . Accessed May 2012

Vargheese R, Prabhudesai P (2014) Securing B2B pervasive information sharing between healthcare providers: enabling the foundation for evidence based medicine. International Workshop on Privacy and Security in HealthCare (PSCare14), pp 525–530

Vleeschouwer C, Delaigle JF, Macq B (2003) Circular interpretation of bijective transformations in lossless watermarking for media asset management. IEEE Trans Multimed 5:97–105

Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error measurement to structural similarity. IEEE Trans Image Process 13(1):600–612

Wang H, Peng D, Wang W, Sharif H, Chen H, Khoynezhad A (2010) Resource-aware secure ECG healthcare monitoring through body sensor networks. Wirel Commun 17(1):12–19

Wong S, Zaremba L, Gooden D, Huang HK (1995) Radiologic image compression—a review. In: Proceedings of the IEEE, vol 83, no 2, pp 194–219

Wu H, Huang J, Shi Y (2015) A reversible data hiding method with contrast enhancement for medical images. J Vis Commun Image Retriev 31:146–153

Yang J, Li J, Niu Y (2015a) A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Gener Comput Syst 43:74–86

Yang J, Qiang Li J, Niu Y (2015b) A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Gener Comput Systems 44:74–86

Zehl L, Jaillet F, Stoewer A, Grewe J, Sobolev A, Wachtler T, Brochier TG, Riehle A, Denker M, Grun S (2016) Handling metadata in a neurophysiology laboratory. Front Neuroinform 10:26

Zhao S, Aggarwal A, Frost R, Bai X (2012) A survey of applications of identity-based cryptography in mobile ad-hoc networks. IEEE Commun Surv Tutor 14(2):380–400

Zheng K, Qian X (2008) Reversible data hiding for electrocardiogram signal based on wavelet transforms. In: Proceedings of international conference on computational intelligence and security

Zhou XQ, Huang HK, Lou SL (2001) Authenticity, and integrity of digital mammography images. IEEE Trans Med Imaging 20:784–791

Zhou Z, Huang HK, Liu BJ (2005) Digital signature embedding (DSE) for medical image integrity in a data grid off-site backup archive. In: Proceedings of SPIE and Medical imaging, pp 306–317

Download references

Author information

Authors and affiliations.

Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran

Hedieh Sajedi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hedieh Sajedi .

Ethics declarations

Conflict of interest.

Author declares that they have no conflict of interest.

Studies with human participants or animals

This article does not contain any studies with human participants or animals performed by the author.

Rights and permissions

Reprints and permissions

About this article

Sajedi, H. Applications of data hiding techniques in medical and healthcare systems: a survey. Netw Model Anal Health Inform Bioinforma 7 , 6 (2018). https://doi.org/10.1007/s13721-018-0169-x

Download citation

Received : 04 November 2017

Revised : 12 April 2018

Accepted : 19 April 2018

Published : 30 April 2018

DOI : https://doi.org/10.1007/s13721-018-0169-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Medical application
• Information hiding
• Steganography
• Watermarking
• Find a journal
• Publish with us
• Track your research

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• Original Research
• Open access
• Published: 30 December 2019

An efficient steganographic technique for hiding data

• Dalia Nashat 1 &
• Loay Mamdouh 1  

Journal of the Egyptian Mathematical Society volume  27 , Article number:  57 ( 2019 ) Cite this article

14k Accesses

20 Citations

3 Altmetric

Metrics details

Steganography is the technique for hiding data and aims to hide data in such a way that any eavesdropper cannot observe any changes in the original media. The least significant bit (LSB) is one of the most public techniques in steganography. The classical technique is LSB substitution. The main idea of this technique is to directly alter some LSB of the cover image with the secret data. The essential drawback of the available LSB techniques is that increasing the capacity of the stego image leads to decreasing its quality. Therefore, the goal of the proposed method is to enhance the capacity taking high visual quality into consideration. To achieve this goal, some LSB of the cover image are inverted depending on the secret data for embedding instead of replacing LSB with the secret data. First, the maximum and minimum values in the secret data are determined then subtract all values of the secret data from this maximum value. Finally, make a division for the results and embed the new results into the cover image to obtain the stego image. The results show that the proposed method gives high capacity and good imperceptibility in comparison with the previous methods.

Introduction

The rapid growth in the development of modern communication technology has made data transmission easier and faster. However, this made the transmitted data are easier to be copied, modified, or destroyed by unauthorized users or for unauthorized access by eavesdropper, attacker, and etc. Thus, protecting the secrecy of data whether in its place or during transmission is a critical issue. Data encryption and data hiding are two major techniques of information security to maintain data secrecy.

Encryption is a data transformation that encrypted data into cipher text and a meaningless form which no one can read it unless who has the key to decrypt the encryption. Data hiding techniques concern with hiding secret data within a carrier in an invisible manner. The carrier can be digital media such as text, audio, image, video, and multimedia and called the cover for secret data. Data hiding has two main branches, steganography and watermarking. The present work focuses on steganography and use images as the cover for hiding secret data. Steganography conceals the secret data inside the cover image in such away which no one can even know there is a secret data there.

Image steganography is common and used most widely with the comparison of other types of steganography. This popularity because images have a large amount of redundant data that can be used to hide secret data easily, and because images take into consideration the advantage of the limited power of the human visual system (HVS) [ 1 – 4 ]. In image steganography, the original image called the cover image, the stego image is the image that results from embedding secret data inside the original image. The cover and stego images should be more similar, so it will be harder for an unauthorized person to know the stego image.

There are many steganographic approaches for hiding data. The most famous steganographic approach is the least significant bit (LSB) where LSB refers to the last or the right-most bit in a binary number. This approach replaces some LSBs of the cover image with the secret data bits of the hidden message. LSB is easy and simple in computations but the capacity is low. Simple LSB approach is also not robust because of the easiness of retrieving the secret message by retrieving the LSBs [ 5 , 6 ]. The LSB inversion approach is preferable because it enhances the stego image quality. In this approach, instead of replacing with secret data, the LSBs of each pixel cover are inverted based on secret data values [ 5 ].

Many improved LSB methods to maximize the capacity with enhancing the stego image quality have been proposed. Sayuthi et al. [ 7 ] proposed a new technique used modulus arithmetic to merge secret data into a cover image instead of direct substitution as applied in normal LSB technique. In this technique, a module is proposed to test the cover image. This method works in a fully spatial domain manner. Pixels are used in a decimal value (0–255).

Aisha and Wilson [ 8 ] presented a simple method to conceal a compressed secret data using arithmetic division operation and various other logical operations within the edges of a color image. First, they used Canny edge detection algorithm to obtain the edges. After that, compressed secret data by using a compression method in the wavelet domain. Then used arithmetic division operation to hide compressed secret data into edges areas of the cover color image. They used PSNR and SSIM to evaluate the stego image quality. Experimental results show that the proposed scheme achieves large embedding capacity and high imperceptibility.

Cheng [ 9 ] introduced an inverted pattern (IP) LSB substitution technique to enhance the stego image quality. This technique combines the processing of secret data before embedding and the processing of stego images after embedding. The technique takes short time to embed secret data into the cover image. The results indicate that this technique has a better quality of stego image than the optimal LSB substitution and the optimal pixel adjustment process (OPAP) LSB substitution methods.

Mohammed and Rossilawati [ 5 ] proposed the bit inversion method to improve the quality of the stego image in color images. This method introduced two levels of security to the standard LSB steganography. The first level is using the two colors, green and blue, instead of using the three colors in the standard LSB. The second level is reversing the bits of pixels of the stego image after applying the standard LSB. The purpose of this method is handling the weaknesses of the standard LSB Steganography besides increasing the capacity.

Orooba [ 10 ] presented a new robust steganographic scheme using adding operation between LSB of pixels in the image and secret data. In the extraction procedure, two keys are used to extract secret data which improve the power of hiding and the difficulty of breaking. Experimental results demonstrate that this technique is a good technique, easy to use and efficient in security. In the future, they intend to develop the system encodes the secret hide text before.

Marghny and Loay [ 11 ] introduced a data hiding technique based on simple LSB substitution scheme for gray images. They partitioned the cover image into two parts. The first for embedding and inverting some bits have the secret bits to enhance the results. The second part indicates which bits are inverted in the first part. After embedding, they apply an optimal LSBs method to improve the quality of stego image. The experimental results indicate that their method has better performance compared to the corresponding methods, in terms of capacity and PSNR.

Khodaei and Faez [ 12 ] presented a new adaptive data-hiding approach for grayscale images. The approach based on LSB substitution and pixel value differencing (PVD) methods. Experimental results indicated that the approach has capacity and stego image quality better than those of Wu et al.’s, Yang et al.’s and Lee et al.’s methods. Also, the approach needs a low time complexity. Also, the approach is secure against the RS detection attack and steganalysis detector using SPAM features.

Vajiheh et al. [ 13 ] proposed an adaptive method which used LSB-M as its embedding method. They identified edges in the cover image and then altered with embedding data in it. The secure locations of an image are determinate by using a complexity measure based on a local neighborhood analysis. Their method gives a better performance which obtaining higher PSNR values with respect to comparable adaptive methods.

In [ 14 ], a new steganography approach to develop a FPPD based distributed steganography is presented. This technique allows for sending multiple secret data components across multiple cover images. A modulus function is added to change the value for a reference pixel. When the secret data is larger than the cover image, another cover image can be used, and so on. The PSNR for this technique is below the original FPPD method. This decrease because modulus function changes the value for reference point.

Kamaldeep and Rajkumar [ 15 ] presented a new scheme based on XOR for hiding data into gray images using three bit XOR steganography system. The maximum no of bits used to hidden data in this technique is equal to X*Y*3, where X and Y are the rows and columns of the image. The time complexity which is equal to O(1) is also calculated. Experimental results show that this scheme exceeds over existing methods and gives good imperceptibility.

Steganographic techniques have three core properties; high capacity, good imperceptibility, and robustness. These three requirements cannot be achieved in one technique, so the sender should consider his priorities [ 16 ]. Therefore, the purpose of the proposed method is to enhance the capacity taking high visual quality into consideration. This is evidenced by comparing the present method with the available steganographic techniques; Cheng [ 9 ], Marghny and Loay [ 11 ] and Khodaei and Faez [ 12 ]. The results show that the proposed method gives high capacity and good imperceptibility in comparison with the previous methods.

To improve the image quality, the value of LSBs are inverted instead of replacing their values with secret data. Arithmetic operations are used to reduce the magnitude of embedded data and hence increasing the capacity. The result of subtracting the minimum number from the maximum number ( R refers to this value) decides the number of bits that will be inverted. This means that according to the R value, the number of LSBs of each pixel that will be inverted in the first part is decided. If this value less than or equal 127 then four LSBs of each pixel will be inverted otherwise five LSBs will be inverted.

The presented method is based on inverting LSBs and some arithmetic operations. Among the arithmetic operations used there is an arithmetic division operation similar to that used in Aisha and Wilson [ 8 ] method. But in the presented method, before applying the division operation, first the maximum and minimum values in the secret data are determined and then subtract the minimum and all values of the secret data from this maximum value. After that, apply a division operation for the results by 32 and then 8. Finally, embed the results by inverting the value of LSBs of the grayscale image. While in the [ 8 ] method, the divisor is 8. First, they used the Canny edge detection algorithm and a dynamically generated threshold to obtain the edges in the cover image. Then, compress the secret data using a compression method in the wavelet domain. Finally, they apply a division operation and logical operations to embed the secret data in the edges of the color image. The division by 32 then 8 increase the security of the proposed technique which is better than division by 8. By this way, the unauthorized user needs to know two quotients, the reminder and also makes four mathematical operations (i.e., two multiplication and two addition) to extract the secret data. While in division by 8, he needs only one quotient, the reminder and makes two mathematical operations (i.e., one multiplication and one addition).

The paper is organized such that in “ The Optimal LSBs technique ” section the optimal LSB method is presented. “ The proposed method ” section explains the proposed method. The results are discussed in “ Experimental results ” section. Finally, the conclusion is introduced in the “ Conclusion ” section.

The Optimal LSBs technique

There are many improvement techniques based on LSBs method are proposed to enhance the quality of the stego image [ 17 – 19 ]. In this section, one of the improved techniques called the optimal LSBs technique [ 20 ] is explained. It applies an optimal pixel adjustment process (OPAP) to enhance the stego image quality. Three candidates, each with the secret data embedded in, for each pixel are selected and compared to choose the closest value to the original pixel value. The best candidate is the optimal pixel and is used to embed the secret data in it [ 20 , 21 ]. The steps for embedding algorithm as follows:

Let H i is the value of the i th pixel in the cover image, t is the number of the secret data bit(s) to be embedded.

Use LSBs method to embed t bit(s) into H i to obtain the stego pixel \(H^{'}_{i}\) .

Adjust the ( t +1)th bit of \(H^{'}_{i}\) to generate another two pixel values \(H^{'}_{-}\) and \(H^{'}_{+}\) that can be estimated as follows:

\(H^{'}_{i}\) , \(H^{'}_{+}\) , and \(H^{'}_{-}\) have the same embedded data because they have the same last t bits.

Apply the following formula to obtain the most approximate \(H^{''}_{i}\) (optimal candidate) to the original pixel value:

Finally, replace all the optimal candidates \(H^{''}_{i}\) with the original pixel values H i .

To explain how the optimal LSBs method can decrease the distortion caused by the simple LSBs method, the following simple example is explained. Suppose H i =10, t =3, and the three secret data bits are 111. Then, the stego pixel \(H^{'}_{i} =15\) is produced after using the simple 3-LSBs method. Adjusting the 4-th bit of \(H^{'}_{i}\) to produce the two pixel values \(H^{'}_{+}=23\) and \(H^{'}_{-}=7\) . \(H^{'}_{i} =15\) , \(H^{'}_{+}=23\) and \(H^{'}_{-}=7\) have the same last three bits of pixel values. The closest value to the original pixel value is \(H^{'}_{-}=7\) which is the optimal candidate. This example shows that the optimal LSBs method improves the stego image quality.

The proposed method

The proposed method will be explained in this section. This method as mentioned before based on inverting LSBs and some arithmetic operations. The values of LSBs of cover pixels are inverted instead of replacing them with secret bits. This improves the stego image quality. Therefore, a flag is used to indicate which bits are inverted [ 8 , 11 ]. The arithmetic operations are finding the maximum and minimum values, subtraction and division respectively. These arithmetic operations are used to reduce the magnitude of embedded data and increasing capacity. Quotients and remainder which are resulted from division operations are stored in quotients and remainder arrays respectively. The divisors 32 and 8 are used respectively, so the maximum size for each quotients can be 3 and 2 bits respectively and for the remainder is 3 bits [ 8 ]. The cover image is partitioned into two equal parts. The first part used for embedding quotients by inverting LSBs of pixels. Also, in this part some inverted LSBs will be inverted again to enhance the results. The second part used for embedding remainder by inverting LSBs of pixels. Also, this part used to indicate the number of bits that used for embedding for each pixel in the first part and which inverted bits are inverted again. The following subsections explain the steps for hiding secret data (the embedding algorithm) and the steps for retrieving secret data (the extracting algorithm). In the following, a description of the image used in the proposed method is given.

Assume I is any gray image, and consists of set of pixels I ={ P 1 ,..., P N }. Every pixel composed of 8 bits:

The image size is computed as

Where H , W is the height and width of the image respectively. Assume M and n are the secret data bits and it’s length respectively,

And h is the maximum hiding capacity in the image I and computed in terms of bits as

The embedding algorithm

In the embedding, the input is a grayscale cover image and a series of pseudo-random data or grayscale image as secret data. The output is a grayscale stego image. Let A and B are the quotient arrays and C is the remainder array. The maximum size of each array is half the size of the cover image.

Find the maximum and minimum value in the secret data, the decimal value ranging between 0 and 255 is used.

Subtract the minimum and each value of secret data from the maximum value.

Divide the result by 32, then store quotient in array A and divide the remainder by 8.

Store quotient, from the second division, in array B and the remainder in array C . Note: divide also the maximum value by 32 and 8 respectively and store results in the first pixel of A , B and C .

Embed quotients of the maximum value in five LSBs of the first pixel in the first part of cover image by inverting values of this five LSBs as follows:

If B(1,1) =1, then invert the value of first LSB.

If B(1,1) =2, then invert the value of second LSB.

If B(1,1) =3, then invert the values of first and second LSBs.

If A(1,1) =1, then invert the value of third LSB.

If A(1,1) =2, then invert the value of fourth LSB.

If A(1,1) =3, then invert the values of third and fourth LSBs.

If A(1,1) =4, then invert the value of fifth LSB.

If A(1,1) =5, then invert the values of third and fifth LSBs.

If A(1,1) =6, then invert the values of fourth and fifth LSBs.

If A(1,1) =7, then invert the value of third, fourth, and fifth LSBs.

Embed remainder of the maximum value in three LSBs of the first pixel in the second part of the cover image as explained in the previous step.

Use the fourth bit of this pixel as an indicator for the value of R . If this value less than or equal 127 then the four LSBs of each pixel is inverted in the first part for embedding, otherwise five LSBs is inverted for embedding. Because the result of dividing R value in range 0 to 127 by 32 needs 2 bits for representation, while dividing R value above 127 needs three bits for representation. Therefore, 127 is a sensitive value that differentiates between 2 and 3 bits representation.

Apply the optimal LSBs method to pixels obtained in steps 5 and 7.

Repeat steps 5 and 6 to embed each pixel in arrays A , B, and C in pixels of the cover image.

Invert some inverted bits again for each pixel in the first part. There are two cases; one or two inverted bits will be inverted again. This will be decided according to the value of R .

Note: for each pixel in the first part, after embedding, there are some inverted bits will be inverted again to improve the results. For illustration, if the value of R less than or equal 127 then two cases are applied. The first case inverts again the second LSB. The second case inverts again the second and fourth LSBs. If the value of R greater than 127 then another two cases will be applied. The first case inverts again the third LSB. The second case inverts again the third and fourth LSBs. Hence, the fourth bit of each pixel in the second part is used as a flag to determine which inverted bits will be inverted again in the corresponding pixel in the first part.

Each pixel in the first part obtained from the previous step has two values. Apply the optimal LSBs method to each value.

Now, there are two values for each pixel after applying the optimal LSBs method, choose the closest to the original value and return 0 or 1 as an indicator to show which invert was selected.

Invert the fourth bit of the pixel of the second part according to this indicator.

Apply the optimal LSBs method to pixel obtained in the previous step.

After embedding all in the cover image, the stego image is generated, then send it to the receiver.

The extracting algorithm

The original image will be required to determine the bits that inverted in each pixel of stego image to recover the secret data. Partition each of the stego and original images into two equal parts and follow the following steps to obtain the secret data.

To know the maximum value follow the following steps from 2 to 6.

Compare the three LSBs of the first pixel in the second part of the stego image with the corresponding in the original image to determine the value of the remainder.

Compare the two LSBs of the first pixel in the first part of the stego image with the corresponding in the original image to determine the value of the quotient.

Multiple the value of this quotient by 8 and add the value of the remainder.

Compare the third, fourth, and fifth LSBs of the first pixel in the first part of the stego image with the corresponding in the original image to determine the value of the quotient.

Multiple the value of this quotient by 32 and add the value obtained in step 4.

Compare the fourth LSB of the first pixel in the second part of the stego image with the corresponding in the original image to determine how many bits are used for embedding for each pixel in the first part.

For each pixel in stego image, repeat the step 2 and compare the fourth LSB with the corresponding in the original image to know which inverted bits are inverted again.

After knowing the inverted bits that are inverted again, restore its value and apply the steps from 3 to 6 and subtract the result from the maximum value to obtain the original value of the secret data.

Repeat the previous step for all pixels to obtain the secret data.

The following is a simple example to further explain the proposed method: assume that the original pixels of the cover image in the first and second parts are 200 and 170 respectively.. The values of secret data are [250, 123, 125] where 250 and 123 are the maximum and minimum values respectively, thus

The value of R is 127.

The result of subtract 125 from 250 is 125, so the value of A =3, B =3, and C =5.

B =3 means inverting the first and second LSBs of the original pixel in the first part. Also, A =3 means inverting the third and fourth LSBs of this pixel.

The value of the stego pixel in the first part will be 197. This value after inverting the second LSB again and apply the optimal LSBs method.

The value of C =5, then invert the first and third LSBs of the original pixel in the second part.

Invert the fourth LSB of this pixel to indicate that the second LSB is inverted again in the pixel in the first part.

The value of the stego pixel in the second part will be 167.

Finally, the stego pixels of the cover image in the first and second parts are 197 and 167 respectively.

Experimental results

To evaluate the performance of the proposed method, extensive experiments are conducted. The seven standard grayscale images “Lena,” “Baboon,” “Pepper,” “Barbara,” “Elaine,” “Cameraman,” and “Tiffany” with different sizes 512×512, 256×256, and 225×225 of each are used as cover images in the experiments. The secret data that are embedded into the cover images are a series of pseudo-random data or grayscale images. MATLAB 2017 is used for programming in the present experiments. The performance evaluation of the proposed method is estimated from two main measuring points: the capacity and the visual quality of stego image.

Capacity is the number of secret data bits that can be embedded into a pixel of the cover media and expressed as number of bits per pixel (bpp). It measures the performance of the method to the amount of data that can be embedded within it. The higher the capacity, the better the performance of the method is [ 22 ]. The capacity is estimated as [ 22 ]:

where ∥ U ∥ represents the total number of secret bits embedded into a cover image sized to H × W pixels.

The second measurement is the evaluation of the stego image quality which estimated by using peak signal-to-noise ratio (PSNR). PSNR is used widely in measurements of steganographic method performance. It estimates the similarity between the original image and the stego image [ 11 , 22 , 23 ]. PSNR is expressed in term of a logarithmic decibel scale [ 11 ] and calculated as follows:

Where 255 is the maximum gray level of an 8 bits/pixel monotonic image [ 24 ] and MSE is the mean square error represents the cumulative squared error between the original image and the stego-image [ 23 ]. A lower MSE means better stego image quality with lesser distortion and higher PSNR [ 25 ]. MSE is defined as

Where H and W are the height and width for the cover image respectively and I ij and \(I^{'}_{ij}\) are the pixel values of the cover and stego images respectively.

Increasing capacity conflict with increasing PSNR. In other words, increase the capacity increases the MSE and this affects inversely on the PSNR. Hence, a tradeoff between capacity and PSNR requirements should be made [ 26 ].

Table  1 , Table  2 , and Table  3 show the results of the proposed method for different sizes of images in terms of capacity (in bits) and PSNR values at R less than or equal 127 and R is greater than 127. The capacity of the presented method is 1048568 because of using 512×512 grayscale images and partition the cover image into two equal parts. Therefore, each part consists of 131072 pixels. The first pixel of each part is used to know the value of the maximum number by embedding it in these pixels. Therefore 131071 pixels of each part will be used to embed secret data. Also, each value of secret data is represented by 8 bits and these bits are embedded in the two parts of the cover image after making arithmetic operations on it. This means that four bits are used from each pixel of the cover image for embedding except two pixels (the first pixel of each part). Therefore, the capacity can be estimated as 131071×4+131071×4=1048568.

Figures  1 and 2 show the stego images of the presented method at R ≤ 127 and R > 127 respectively. The quality for these stego images is high and the distortion is imperceptible to the human eyes. Table  4 shows comparisons between the presented method at R ≤ 127 and Marghny and Loay [ 11 ] method for the same secret data in terms of PSNR and the same embedding capacity. It’s clear that the proposed method presents higher PSNR values than [ 11 ].

Four stego images of size 512×512 at R ≤ 127: a Lena. b Baboon. c Peppers. d Tiffany

Three stego images of size 512×512 at R > 127: a Barbara. b Elaine. c Cameraman

Another comparison between the results of the method at R ≤ 127 and the results of Khodaei and Faez [ 12 ] method in terms of capacity and PSNR values in Table  5 . There is a slight difference in the value of capacity between the two methods for each image by some bits. This is due to representing each value for the secret data by 8 bits and embed these bits after making the arithmetic operations on it in the two parts of the cover image except the first pixel of each part. From the comparison, it is noticed that at the same embedding capacity approximately, the proposed method provides better values of PSNR than [ 12 ] method. This means that the present method outperforms on Khodaei and Faez [ 12 ] method in performance and efficiency.

Table  6 presents a comparison between the presented method at R > 127 and Cheng [ 9 ] method for the same secret data (Tiffany image) in terms of embedding capacity and PSNR. The capacity for the presented method is less than the Cheng [ 9 ] method by one byte. In the present method the capacity for “lena” and “Baboon” is 1048568 while in Cheng [ 9 ] method is 1048576. This because, in the present method, the first pixel of each part (i.e., the first and the second parts) is used to embed the value of the maximum number. This decrease the capacity by one byte. The comparison demonstrates that at the same capacity approximately, PSNR of the proposed method is better than Cheng [ 9 ] method.

The complexity of the proposed method is the computational cost of embedding the secret data into a cover image and extracting them from stego image in the LSB algorithm. Although, the algorithm complexity increases with increasing the number of used LSBs, the LSB embedding algorithm has linear time complexity O ( n ) [ 27 ]. This means that embedding and extracting the secret data takes the lowest time among other algorithms like EzStego algorithm [ 28 ] which takes O ( n 2 ) [ 29 ].

In this research, an efficient steganographic method used inverting LSBs and arithmetic operations is proposed. The cover image is partitioned into two equal parts and the difference between the maximum and minimum value of secret data determines if we use four LSBs or five LSBs of each pixel in the first part for embedding. Also, this difference determines which two cases will be applied to the inverted bits to invert again. Standard grayscale images are used to evaluate the performance of the proposed technique. Experimental results indicate that the present method increases the embedding capacity and enhances the quality of the stego image. In further research, besides the merits obtained in this paper, increasing the robustness property will be taken into consideration.

Availability of data and materials

The datasets analyzed during the current study are available in the MATLAB Image Processing Toolbox.

Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: Digital image steganography: survey and analysis of current methods. Signal Process. 90(3), 727–752 (2010).

Article   Google Scholar  

Gutub, A., Al-Qahtani, A., Tabakh, A.: Triple-a: Secure rgb image steganography based on randomization. In: The 7thACS/IEEE International Conference on Computer Systems and Applications , pp. 400–403. IEEE, Rabat (2009). 10–13 May 2009.

Google Scholar  

Amanpreet, K., Renu, D., Geeta, S.: A new Image Steganography Based on First Component Alteration Technique. Int. Comput. Sci. Inf. Secur. 6(3) (2009).

Bhattacharyya, D., Roy, A., Roy, P., Kim, T. -h.: Receiver compatible data hiding in color image. Int. J. Adv. Sci. Technol. 6(1), 15–24 (2009).

Majeed, M. A., Sulaiman, R.: An improved lsb image steganography technique using bit-inverse in 24 bit colour image. J. Theoret. Appl. Inf. Technol. 80(2) (2015).

Akhtar, N., Khan, S., Johri, P.: An improved inverted lsb image steganography. In: IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) , pp. 749–755. IEEE, Ghaziabad (2014).

Jaafar, S., Manaf, A. A., Zeki, A. M.: Steganography technique using modulus arithmetic. In: In 2007 9th International Symposium on Signal Processing and Its Applications , pp. 1–4. IEEE (2007).

Fernandes, A., Jeberson, W.: Covert communication using arithmetic division operation. Proc. Comput. Sci. 45, 354–360 (2015).

Yang, C. -H.: Inverted pattern approach to improve image quality of information hiding by lsb substitution. Pattern Recogn. 41(8), 2674–2683 (2008).

Al-Farraji, O. I. I.: Steganography by use binary operations. Int. J. Engineer. Res. Gen. Sci. 4, 179–187 (2016).

Mohamed, M. H., Mohamed, L. M.: High capacity image steganography technique based on lsb substitution method. Appl. Math. Inf. Sci. 10(1), 259 (2016).

Article   MathSciNet   Google Scholar  

Khodaei, M., Faez, K.: New adaptive steganographic method using least-significant-bit substitution and pixel-value differencing. IET Image Process. 6(6), 677–686 (2012).

Sabeti, V., Samavi, S., Shirani, S.: An adaptive lsb matching steganography based on octonary complexity measure. Multimed. Tools Appl. 64(3), 777–793 (2013).

Wibisurya, A., et al.: Distributed steganography using five pixel pair differencing and modulus function. Proc. Comput. Sci. 116, 334–341 (2017).

Joshi, K., Yadav, R.: New approach toward data hiding using xor for image steganography. In: Proceedings of the Ninth International Conference on Contemporary Computing (IC3) , pp. 1–6. IEEE, Noida (2016).

Grı̄bermans, D., Jeršovs, A., Rusakovs, P.: Development of requirements specification for steganographic systems. Appl. Comput. Syst. 20(1), 40–48 (2016).

Thien, C. -C., Lin, J. -C.: A simple and high-hiding capacity method for hiding digit-by-digit data in images based on modulus function. Pattern Recogn. 36(12), 2875–2881 (2003).

Wang, R. -Z., Lin, C. -F., Lin, J. -C.: Image hiding by optimal lsb substitution and genetic algorithm. Pattern Recogn. 34(3), 671–683 (2001).

Wang, S. -J.: Steganography of capacity required using modulo operator for embedding secret image. Appl. Math. Comput. 164(1), 99–116 (2005).

MathSciNet   MATH   Google Scholar  

Chan, C. -K., Cheng, L. -M.: Hiding data in images by simple lsb substitution. Pattern Recogn. 37(3), 469–474 (2004).

Wu, N. -I., Hwang, M. -S.: Data hiding: current status and key issues. IJ Netw. Secur. 4(1), 1–9 (2007).

Chang, C. -C., Chou, Y. -C., Kieu, T. D.: Information hiding in dual images with reversibility. In: Proceedings of the Third International Conference on Multimedia and Ubiquitous Engineering , pp. 145–152. IEEE, Qingdao (2009).

Kamau, G. M.: An enhanced least significant bit steganographic method for information hiding. PhD thesis (2014).

Thung, K. -H., Raveendran, P.: A survey of image quality measures. In: International Conference for Technical Postgraduates (TECHPOS) , pp. 1–4. IEEE, Kuala Lumpur (2009).

Jiansheng, M., Sukang, L., Xiaomei, T.: A digital watermarking algorithm based on dct and dwt. In: Proceedings. The 2009 International Symposium on Web Information Systems and Applications (WISA 2009) , p. 104. Academy Publisher (2009).

Al-Ataby, A., Al-Naima, F.: A modified high capacity image steganography technique based on wavelet transform. Int. Arab J. Inf. Technol. 7(4), 358–364 (2010).

Zeeshan, M., Ullah, S., Anayat, S., Hussain, R. G., Nasir, N.: A review study on unique way of information hiding: Steganography. Int. J. Data Sci. Technol. 3(5), 45–51 (2017).

Munir, R.: Chaos-based modified “ezstego” algorithm for improving security of message hiding in gif image. In: 2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA) , pp. 80–84. IEEE (2015).

Kemal Tutuncu, BD: Adaptive lsb steganography based on chaos theory and random distortion. Adv. Electr. Comput. Engineer. 18(3), 15–22 (2018).

Download references

Acknowledgements

The authors would like to express their sincere appreciation to Prof. Dr. Taha Morsi Elgindy, Mathematics Department, Assuit University, for providing valuable support and worthy guidance during this research.

There is no financial support for this research.

Author information

Authors and affiliations.

Mathematics Department, Faculty of Science, Assuit University, Assuit, Egypt

Dalia Nashat & Loay Mamdouh

You can also search for this author in PubMed   Google Scholar

Contributions

Both of authors cooperated in all processes of this research.

Corresponding author

Correspondence to Dalia Nashat .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Nashat, D., Mamdouh, L. An efficient steganographic technique for hiding data. J Egypt Math Soc 27 , 57 (2019). https://doi.org/10.1186/s42787-019-0061-6

Download citation

Received : 14 June 2019

Accepted : 02 December 2019

Published : 30 December 2019

DOI : https://doi.org/10.1186/s42787-019-0061-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Information security
• Image processing
• Steganography
• Data hiding

Data Hiding in Flying Ad Hoc Networks

Campus location, principal supervisor, additional supervisor 1, year of award, department, school or centre, degree type, usage metrics.

• System and network security

• Liberty University
• Jerry Falwell Library
• Special Collections
• < Previous

Home > ETD > Doctoral > 6047

Doctoral Dissertations and Projects

Examining the impact of special education teacher attrition on student performance: a causal-comparative study.

Kiandra Dane Jones , Liberty University Follow

School of Education

Doctor of Philosophy in Education (PhD)

Rebecca Lunde

attrition, prosocial classroom, student achievement, students with disabilities, burnout

Disciplines

Education | Special Education and Teaching

Recommended Citation

Jones, Kiandra Dane, "Examining the Impact of Special Education Teacher Attrition on Student Performance: A Causal-Comparative Study" (2024). Doctoral Dissertations and Projects . 6047. https://digitalcommons.liberty.edu/doctoral/6047

The purpose of this quantitative, causal-comparative study was to determine if there was a difference between the achievement of Georgia special education students on the Ninth Grade Literature and American Literature Georgia Milestones Test in school districts with high and low special education teacher (SET) attrition rates. This study provided quantifiable data that measured the impact of teacher burnout on student achievement. This research further supported the literature in this field by documenting the consequences of increasing teacher turnover rates. Participants in this study included Georgia Milestones student achievement data from approximately 180 Georgia school districts from 2019–2022. The state’s SET attrition data accounts for approximately 114,800 teachers. By providing quantifiable data reflecting the impact of teacher burnout on student achievement, the literature documenting the consequences of growing teacher attrition rates was supported. Two independent samples t-tests were conducted to determine if there was a difference between student achievement scores and school districts with high or low teacher attrition rates. The researcher determined that there was no significant difference between Georgia Literature Milestones student achievement scores and SET rates between school districts with high and low attrition rates. Results from this study may also assist leaders by providing a different perspective and strategic approach when seeking to improve Georgia Milestones student achievement data.

Included in

Special Education and Teaching Commons

• Collections
• Faculty Expert Gallery
• Theses and Dissertations
• Conferences and Events
• Open Educational Resources (OER)
• Explore Disciplines

Advanced Search

• Notify me via email or RSS .

Faculty Authors

• Submit Research
• Expert Gallery Login

Student Authors

• Undergraduate Submissions
• Graduate Submissions
• Honors Submissions

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

• Youtube Channel |
• Undergraduate (S1)
• Master Study (S2)
• Lecturer and Staff
• Teaching Laboratory
• Research Laboratory
• Management & Development
• Collaborations
• Internship (PKLI) 2022
• Thesis (Skripsi) – S1
• Curriculum For Master
• Master Thesis
• Student Publication For Master
• Curriculum Evaluation
• Program Mikrotik Academy
• University Regulations
• Government Regulations
• PUBLICATIONS
• Intellectual Property Rights
• Journal Scopus
• Product PKL
• Software House
• Online Aplications
• Community Service
• Organization & Communities
• Achievements
• Scholarships
• Student Facilities
• Accreditation Certificate For Undergraduate (S1)
• Accreditation Certificate For Master (S2)

Data of Thesis Supervisor for Odd 2024/2025 – 2

Based on the Pra-Proposal Data Form that has been collected, we have determined the supervisors as follows:

Related Posts

Data of internship (pkl) supervisor for odd semester 2024-2025 stage 3, schedule for comprehensive exam september, seminar on results period i for odd t.a 2024/2025 in september stage 3, seminar on results period i for odd t.a 2024/2025 in september stage 2, data of internship (pkl) supervisor for odd semester 2024-2025 stage 2, seminar on results period i for odd t.a 2024/2025 in september, results selection assistants of practicum for odd semester 2024/2025, recruitment of practicum assistants for odd semester 2024/2025, data of internship (pkl) supervisor for odd semester 2024-2025, data of thesis supervisor for odd 2024/2025.