Hybrid Approach for Securing Image Tempering in Cloud Storage
Keywords:
Permutation ordered binary (POB) number system, secret sharing, encrypted domainAbstract
The global appeal of leveraging advanced computational infrastructure provided by cloud-based multimedia systems has become increasingly evident. Nevertheless, the security challenges associated with these cloud-based systems, which involve third-party servers, cannot be overlooked. To tackle these security concerns effectively, a proposed solution involves encrypting the data in a way that makes it indecipherable to the cloud data centers. This work introduces an image encryption method built upon the Permutation Ordered Binary (POB) Number System. This technique entails the partitioning of the image into randomly generated shares, which can subsequently be stored in cloud data centers. Furthermore, the proposed method guarantees the integrity of these shares at the individual pixel level. If any unauthorized modifications occur on the cloud servers, the system can accurately detect the altered pixels by leveraging authentication bits and precisely locate the tampered area. This tampered section is also reflected in the reconstructed image, which is accessible to authorized users. The accuracy of tamper detection has been thoroughly evaluated on a pixel-by-pixel basis, demonstrating remarkable effectiveness in a wide range of tampering scenarios.
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References
Christian Riess and Tiago Jose de Carvalho,“Exposing Digital Image Forgeries by Illumination Color Classification”, IEEE Transactions On Information Forensics And Security, vol. 8,2013
Tu K.Huynh, Thuong Le-Tien, Khoa V.Huynh, Sy C.Nguyen A Survey on Image Forgery Detection Techniques, The 2015 IEEE RIVF International Conference on Computing & Communication Technologies Research, Innovation, and Vision for Future (RIVF), DOI 978-1-4799-8044-4/15
U. Tembe, S. Thombre, “Survey of Copy-Paste Forgery Detection in Digital Image Forensic”, International Conference on Innovative Mechanisms for Industry Applications (2017)
Asghar K, Habib Z, Hussain M (2017) Copy-move and splicing image forgery detection and localization techniques: a review. Aust J Forensic Sci 49(3):281–307
K. Asghar, Z. Habib, and M. Hussain, “Copy-move and splicing image forgery detection and localization techniques: a review,” Australian Journal of Forensic Sciences, vol. 49, no. 3, pp. 281–307, 2017. DOI: 10. 1080/00450618.2016.1153711.
Gajanan K. Birajdar, Vijay H. Mankar Digital image forgery detection using passive techniques: A survey, Digital Investigation 10(3): 226- 245 (2013).
Bo Xu, Guangjie Liu, and Yuewei Dai , “Detecting Image Splicing Using Merged Features in Chroma Space”, the Scientific World Journal”,2014.
T. Yuan, S. Zhang, Secure fault tolerance in wireless sensor networks, in: CITWORKSHOPS’08: Proceedings of the 2008 IEEE Eighth International Conference on Computer and Information Technology Workshops, 2008, pp. 477–482.
W. Zhang, G. Cao, T.L. Porta, Data dissemination with ring-based index for sensor networks, in: IEEE International Conference on Network Protocol,November 2003.
Mazhar Ali, Samee U. Khan, and Athanasios V. Vasilakos. 2015. Security in cloud computing: Opportunities and challenges. Inf. Sci. 305 (2015), 357–383
Charles Asmuth and John Bloom. 1983. A modular approach to key safeguarding. IEEE Transactions on Information Theory 29, 2 (1983), 208–210.
Gaurav Bhatnagar, Q. M. Jonathan Wu, and Pradeep K. Atrey. 2013. Secure randomized image watermarking based on singular value decomposition. ACM Trans. Multimedia Comput. Commun. Appl. 10, 1, Article 4(Dec. 2013), 21 pages. http://doi.acm.org/10.1145/2542205.2542207
V. P. Binu and A. Sreekumar. 2014. Generalized secret sharing using permutation ordered binary system. CoRR abs/1407.3609 (2014). http://arxiv.org/abs/1407.3609.
George Robert Blakley. 1979. Safeguarding cryptographic keys. In Proceedings of the National Computer Conference. 313–317.
Dan Bogdanov. 2007. Foundations and properties of shamirs secret sharing scheme research seminar in cryptography. University of Tartu, Institute of Computer Science.
Marco Botta, Davide Cavagnino, and Victor Pomponiu. 2014. Protecting the content integrity of digital imagery with fidelity preservation: An improved version. ACM Trans. Multimedia Comput. Commun. Appl. 10, 3, Article 29 (April 2014), 5 pages. http://doi.acm.org/10.1145/ 2568224.
Zouhair Chiba, Noureddine Abghour, Khalid Moussaid, Amina El Omri, and Mohamed Rida. 2016. A survey of intrusion detection systems for cloud computing environment. In Proceedings of the International Conference on Engineering & MIS (ICEMIS). IEEE, 1–13.
MP Deepika and A Sreekumar. 2016. A novel secret sharing scheme using POB number system and CRT. Int. J. Appl. Eng. Res. 11, 3 (2016), 2049–2054.
Robert H. Deng and Yanjiang Yang. 2009. A study of content authentication in proxy-enabled multimedia delivery systems: Model, techniques, and applications. ACM Trans. Multimedia Comput. Commun. Appl. 5, 4, Article 28 (Nov. 2009), 20 pages. http://doi.acm.org/10.1145/1596990.1596992.
Jiri Fridrich and Miroslav Goljan. 1999. Protection of digital images using self embedding. In Symposium on Content Security and Data Hiding in Digital Media. Newark, NJ.
Singh, P.,Chadha, R.S.:Asurvey of digital watermarking techniques, applications and attacks. IEEE Int. Conf. Ind. Inform. 2, 165–175 (2013)
I. J. Cox, M. L. Miller, J. A. Bloom and C. Honsinger, “Digital watermarking”, San Francisco: Morgan Kaufmann, vol. 1558607145, (2002).
Lu, C., Liao, H.M., Member, S.: Structural digital signature for image authentication: an incidental distortion resistant scheme. IEEE Trans. Multimed. 5, 161–173 (2003)
Zhao and J.Guo,”Passive forensics for copy-move image forgery using a method based on DCT and SVD”,International Conference on forensic science,pp.158-166,2013.
P.Xunyu and L.Siwei,”Region duplication detection using image feature matching”,IEEE Transactions on Information Forensics security,pp.857- 67,2011
B.Mahdian,S.Saic,”Blind methods for detecting image fakery”,IEEE Int. Carnahan conference on security technology,pp.280-6,2008.
G.Muhammad,M.Hussain and G.Bebis,”Passive copy-move forgery detection using undecimated dyadic wavelet transform”,Digital investigation,pp.49-57,2012.
Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detectionscheme. IEEE Trans. Inf. (2015)
Lee, J.C., Chang, C.P., Chen, W.K.: Detection of copy-move image forgery using histogramof orientated gradients. Inf. Sci. (Ny) 321, 250– 262 (2015)
Zhu, Y., Shen, X., Chen, H.: Copy-move forgery detection based on scaled ORB. Multimed.Tools Appl. 75, 3221–3233 (2016)
Huang, D., Huang , C., Hu,W.: Robustness of copy-move forgery detection under high JPEG compression artifacts. Multimed. Tools Appl. 76(1), 1509–1530 (2017)
Popescu, A. and Farid, H. (2004), “Exposing Digital Forgeriesby Detecting Duplicated Image Regions”, Tech. Rep. TR2004- 515,Dartmouth College, Computer Science, Hanover
Al-Sawadi, M. Mohammad, G. Hussain, M. Bebis, G. (2013),“Copy- Move Image Forgery Detection Using Local Binary Patternand Neighborhood Clustering”, Modelling Symposium (EMS),2013 European, (20-22 Nov. 2013), Manchester, pp. 249 – 25.
Bayram, S. Sencar, T. Memon, N. (2009), “An Efficient andRobust Method forDetecting Copy-Move Forgery”, in Proceedingof the IEEE International Conference on Acoustics, Speech, andSignal Processing (ICASSP ’09), pp. 1053–1056, Taipei, Taiwan.
Shao, H. Yu, T. Xu, M. Cui, W. (2012), “Image regionduplication detection based on circular window expansion andphase correlation”, Forensic Science International, vol. 222, no. 1–3, pp. 71–82.
Singh D, Singh SK (2006) Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability. J Vis Commun Image Represent 38(Jul 2016):775–789
Tai W-L, Liao Z-J (2018) Image self-recovery with watermark self-embedding. Signal Process. Signal Process Image Commun 65(Jul. 2018):11–25
Sasikaladevi N, Geetha K, Mahalakshmi N et al (2019) SNAP-compressive lossless sensitive image authentication and protection scheme based on Genus-2 hyper elliptic curve. Multimed Tools Appl 78: 26163–26179
Chamlawi R, Khan A (2010) Digital image authentication and recovery: Employing integer transform based information embedding and extraction. Inf Sci 180(Dec. 2010):24
Afjal MI, Mamun MdA, Uddin MdP (2019) Band reordering heuristics for lossless satellite image compression with 3D-CALIC and CCSDS. J Vis Commun Image Represent 59(Feb. 2019):514–526.
Kumar, Vimal, and Rakesh Kumar. "A cooperative black hole node detection and mitigation approach for MANETs." In Innovative Security Solutions for Information Technology and Communications: 8th International Conference, SECITC 2015, Bucharest, Romania, June 11-12, 2015. Revised Selected Papers 8, pp. 171-183. Springer International Publishing, 2015.
Kumar, V., Shankar, M., Tripathi, A.M., Yadav, V., Rai, A.K., Khan, U. and Rahul, M., 2022. Prevention of Blackhole Attack in MANET using Certificateless Signature Scheme. Journal of Scientific & Industrial Research, 81(10), pp.1061-1072.
Kumar, V. and Kumar, R., 2015. An adaptive approach for detection of blackhole attack in mobile ad hoc network. Procedia Computer Science, 48, pp.472-479.
Kumar, V. and Kumar, R., 2015, April. Detection of phishing attack using visual cryptography in ad hoc network. In 2015 International Conference on Communications and Signal Processing (ICCSP) (pp. 1021-1025). IEEE.
Kumar, V. and Kumar, R., 2015. An optimal authentication protocol using certificateless ID-based signature in MANET. In Security in Computing and Communications: Third International Symposium, SSCC 2015, Kochi, India, August 10-13, 2015. Proceedings 3 (pp. 110-121). Springer International Publishing.
Kumar, V. and Kumar, R., 2017. Prevention of blackhole attack using certificateless signature (CLS) scheme in MANET. In Security Solutions for Hyperconnectivity and the Internet of Things (pp. 130-150). IGI Global.
Gupta, P., Kumar, V. and Yadav, V., 2021. Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 8(29), pp.e1-e1.
Narayan, Vipul, et al. "A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction.
Mall, Pawan Kumar, et al. "A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities." Healthcare Analytics (2023): 100216.
Narayan, Vipul, et al. "Severity of Lumpy Disease detection based on Deep Learning Technique." 2023 International Conference on Disruptive Technologies (ICDT). IEEE, 2023.
Saxena, Aditya, et al. "Comparative Analysis Of AI Regression And Classification Models For Predicting House Damages İn Nepal: Proposed Architectures And Techniques." Journal of Pharmaceutical Negative Results (2022): 6203-6215.
36. Kumar, Vaibhav, et al. "A Machine Learning Approach For Predicting Onset And Progression"“Towards Early Detection Of Chronic Diseases “." Journal of Pharmaceutical Negative Results (2022): 6195-6202.
37. Chaturvedi, Pooja, A. K. Daniel, and Vipul Narayan. "A Novel Heuristic for Maximizing Lifetime of Target Coverage in Wireless Sensor Networks." Advanced Wireless Communication and Sensor Networks. Chapman and Hall/CRC 227-242.
Deshwal, V., Kumar, V., Shukla, R. and Yadav, V., 2022. Estimating COVID-19 Cases Using Machine Learning Regression Algorithms. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 15(5), pp.390-400
Mr. Anish Dhabliya. (2013). Ultra Wide Band Pulse Generation Using Advanced Design System Software . International Journal of New Practices in Management and Engineering, 2(02), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/14
Singh, M. ., Angurala, D. M. ., & Bala, D. M. . (2020). Bone Tumour detection Using Feature Extraction with Classification by Deep Learning Techniques. Research Journal of Computer Systems and Engineering, 1(1), 23–27. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/21
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