Skip to main content
Log in

Verifying the Images Authenticity in Cognitive Internet of Things (CIoT)-Oriented Cyber Physical System

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

With the recent development of Cognitive Internet of Things (CIoT) and the potential of Cyber Physical System (CPS), people’s daily activities become smarter, and intelligent. The combination of CIoT and CPS can greatly enhance the quality of people’s life. To this end, this article proposes CIoT-CPS that comprises of two main models: user activity cognitive model and image authentication model.The user activity cognitive model (UACM) is a machine-learning model to have the meaningful data. The image authentication model is to verify the authenticity of images captured by various devices, such as smart phones, digital cameras, and other camera-embedded portable devices. The authenticity of an image is breached when parts of images are assembled to produce a new image (known as a splicing forgery), or a part of an image is copied or pasted into another part of the same image (known as a copy-move forgery). In the proposed verification method, an opposite color local binary pattern (OC-LBP) texture descriptor is applied to a questioned image. The image is first decomposed into an RGB (red, green, blue) and a luminance and chroma color spaces. The OC-LBP measures the interrelation between pixels of different color components. The intensive computation involving six color components and a gray version is performed in the cloud, where a server can be dedicated to doing this job. The histograms of the OC-LBP are concatenated with weights to produce a final feature vector of the image. A support vector machine is applied as a classifier, which classifies the image as authentic or forged. Several experiments were performed to verify the suitability of those models or approaches. The proposed approaches show a good accuracy compared to other competing approaches.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Chen M, Yang J, Hao Y, Mao S, Hwang K (2017) A 5g cognitive system for healthcare. Big Data Cogn Comput 1(1):1–5

    Google Scholar 

  2. Hwang K, Chen Min (2017) Big data analytics for cloud or iot and cognitive computing. Wiley, UK. ISBN: 9781119247029

    Google Scholar 

  3. Vlacheas P et al (2013) Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun Mag 51(6):102–111

    Article  Google Scholar 

  4. Zhang M et al (2012) Cognitive internet of things: concepts and application example. Int J Comput Sci Issues 9(3):151–158

    Google Scholar 

  5. Wu Q et al (2014) Cognitive internet of things: a new paradigm beyond connection. IEEE Int Things J 1(2):129–143

    Article  Google Scholar 

  6. Chen M, Ma Y, Li Y, Wu D, Zhang Y, Youn C (2017) Wearable 2.0: enable human-cloud integration in next generation healthcare system. IEEE Commun 55(1):54–61

    Article  Google Scholar 

  7. Chen M, Yang J, Zhu X, Wang X, Liu M, Song J (2017) Smart home 2.0: innovative smart home system powered by botanical iot and emotion detection. Mobile Networks and Applications https://doi.org/10.1007/s11036-017-0866-1

  8. Wan J, Cai H, Zhou K (2015) Industrie 4.0 enabling technologies. In: Proceedings of the international conference on intelligent computing and internet of things. Harbin, pp 135–140

  9. Hossain MS, Muhammad G (2016) Cloud-assisted industrial internet of things (IIoT) - enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  10. Feng S, Setoodeh P, Haykin S (2017) Smart home: cognitive interactive people-centric internet of things. IEEE Commun Mag 55(2):34–39

    Article  Google Scholar 

  11. Hossain MS (2017) Cloud-supported cyber-physical framework for patients monitoring. IEEE Syst J 11(1):118–127

    Article  Google Scholar 

  12. Hossain MS, Rahman MA, Muhammad G (2017) Cyber physical cloud-oriented multi-sensory smart home framework for elderly people: energy efficiency perspective. J Parallel Distrib Comput 103:11–21

    Article  Google Scholar 

  13. Wang J, Abid H, Lee S, Shu L, Xia F (2011) A secured health care application architecture for cyber-physical systems. Control Engineering and Applied Informatics (CEAI) 13(3):101–108

    Google Scholar 

  14. Foteinos V et al (2013) Cognitive management for the internet of things: a framework for enabling autonomous applications. IEEE Vehic Tech Mag 8(4):90–99

    Article  Google Scholar 

  15. Sun Y, Todorovic S, Goodison S (2010) Local learning based feature selection for high dimensional data analysis. IEEE Trans Pattern Anal Machine Intell 32(9):1610–1626

    Article  Google Scholar 

  16. AlSawadi M, Muhammad G, Hussain M, Bebis G (2013) Copy-move image forgery detection using local binary pattern and neighborhood clustering. In: European modeling symposium (EMS). Manchester, UK

  17. Chang CC, Lin CJ (2010) LIBSVM—a library for support vector machine. http://www.csie.ntu.edu.tw/cjlin/libsvm

  18. CASIA tampered image detection evaluation database. Downloadable at: http://forensics.idealtest.org

  19. Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vis Appl 25(4):985–995

    Article  Google Scholar 

  20. Wei W, Jing D, Tieniu T (2010) Image tampering detection based on stationary distribution of Markov chain. In: IEEE international conference on image processing (ICIP’10), pp 2101–2104

  21. Hossain MS, Muhammad G (2016) Authenticated media uploading framework for mobile cloud computing. Memetic Comput 8(4):325–332

    Article  Google Scholar 

  22. He Z, Lu W, Sun W, Jiwu H (2012) Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299

    Article  Google Scholar 

  23. Al-Hammadi MH, Muhammad G, Hussain M, Bebis G (2013) Curvelet transform and local texture based image forgery detection. In: Bebis G et al (eds) International symposium on visual computing (ISVC’13), Crete, Greece, July 29–31, 2013; ISVC 2013, Part II, LNCS 8034, pp 503–512

  24. Hussain M, Muhammad G, Saleh SQ, Mirza AM, Bebis G (2013) Image forgery detection using multi-resolution weber local descriptors. In: Eurocon2013. Zagreb, Croatia, pp 1570– 1577

Download references

Acknowledgments

The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this paper through the Vice Deanship of Scientific Research Chairs.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Shamim Hossain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hossain, M.S., Muhammad, G. & AL Qurishi, M. Verifying the Images Authenticity in Cognitive Internet of Things (CIoT)-Oriented Cyber Physical System. Mobile Netw Appl 23, 239–250 (2018). https://doi.org/10.1007/s11036-017-0928-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-017-0928-4

Keywords

Navigation