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Object detection framework to generate secret shares

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Abstract

Nowadays, the sharing of images via the Internet has been widely used. The security concern during transmission of images has been a very important issue, as images contain a lot of crucial information. To prevent access to this information by any unauthorized user various encryption schemes have been developed. An image may consist of a large number of pixels and the encryption of the whole image takes more time. To overcome this problem, we proposed an algorithm that focuses on encryption of only those objects which contain the major information of an image instead of encrypting the complete image, this saves the time required in encryption as only the objects contained in the image are encrypted. The proposed algorithm exploits the use of object detection and image secret sharing. Object detection is done using the “You Only Look Once (YOLO)” algorithm. Further, the objects detected are encrypted using (n,n) modular arithmetic secret sharing scheme. The quantitative measures like correlation, SSIM, RMSE, PSNR has been used to evaluate the performance of the proposed algorithm on COCO dataset. The experimental results show that the proposed algorithm is lossless i.e. original and reconstructed images are exactly the same. The proposed algorithm is efficient and can be used in a broad range of applications.

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References

  1. Arraziqi D, Haq ES (2019) “Optimization of video steganography with additional compression and encryption”. TELKOMNIKA 17(3):1417–1424

    Article  Google Scholar 

  2. Brassington G (2017) “Mean absolute error and root mean square error: which is the better metric for assessing model performance?.” EGU General Assembly Conference Abstracts, Vol. 19 pp.35–74

  3. Brodal GS, Moruz G, Stølting G (2006) “Skewed binary search trees”. European Symposium on Algorithms, Springer, Berlin Heidelberg

    Book  Google Scholar 

  4. Carlson JA (2017) “Method for secure communication using asymmetric and symmetric encryption over insecure communications.” U.S Patent No. 9,819,656. 14 Nov.

  5. Cecotti H, Gardiner B (2016) “Classification of images using semi-supervised learning and structural similarity measure” Irish Machine Vision and Image Processing Conference Irish Pattern Recognition and Classification Society

  6. Chen T-H, Wu C-S (2011) “Efficient multi-secret image sharing based on Boolean operations”. Signal Processing 91(1):90–97

    Article  Google Scholar 

  7. Cox I, et al. (2007) Digital watermarking and steganography Morgan kaufmann

  8. COCO Dataset: http://cocodataset.org/

  9. Dai J, et al. (2016) “R-fcn: Object detection via region-based fully convolutional networks.” Advances in neural information processing systems

  10. Dalal N, Triggs B (2005) “Histograms of oriented gradients for human detection.” International Conference on computer vision & Pattern Recognition (CVPR’05). Vol. 1 IEEE computer society

  11. Deshmukh M, Nain N, Ahmed M (2016) “An (n, n)-multi secret image sharing scheme using boolean XOR and modular arithmetic”. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA). IEEE

  12. Deshmukh M, Nain N, Ahmed M (2017) “A novel approach for sharing multiple color images by employing Chinese Remainder Theorem”. Journal of Visual Communication and Image Representation 49:291–302

    Article  Google Scholar 

  13. Deshmukh M, Nain N, Ahmed M (2017) “A Novel Approach of an (n, n) Multi-Secret Image Sharing Scheme Using Additive Modulo.” Proceedings of International Conference on Computer Vision and Image Processing, Springer, Singapore

  14. Deshmukh M, Nain N, Ahmed M (2018) “Secret sharing scheme based on binary trees and Boolean operation.” Knowledge and Information Systems:, vol 60

  15. Di PR, Guarino S (2013) “Data confidentiality and availability via secret sharing and node mobility in UWSN.” 2013 Proceedings IEEE INFOCOM IEEE

  16. Donahue J, et al. (2014) “Decaf: A deep convolutional activation feature for generic visual recognition.” International conference on machine learning

  17. Dong J, et al. (2014) “Towards unified object detection and semantic segmentation,” European Conference on Computer Vision Springer, Cham

  18. Duseja T, Deshmukh M (2019) “Image compression and encryption using chinese remainder theorem.” Multimedia Tools and Applications:, vol 78

  19. Erhan D, et al. (2014) “Scalable object detection using deep neural networks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  20. Farah MA B, Farah A, Farah T (2019) “An image encryption scheme based on a new hybrid chaotic map and optimized substitution box.” Nonlinear Dynamics : 99, 1–24

  21. Farah MA B et al (2020) “A novel chaos based optical image encryption using fractional Fourier transform and DNA sequence operation”. Optics & Laser Technology 121:105777

    Article  Google Scholar 

  22. Felzenszwalb PF, et al. (2010) Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence 32 (9):1627–1645

    Article  Google Scholar 

  23. Feng J-B, et al. (2005) A new multi-secret images sharing scheme using Largranges interpolation. J Syst Softw 76(3):327–339

    Article  Google Scholar 

  24. Forouzan BA (2007) Cryptography & network security. McGraw-Hill Inc.

  25. Girshick R (2015) “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision

  26. Guo T, Liu F, Wu C (2014) “K out of k extended visual cryptography scheme by random grids”. Signal Processing 94:90–101

    Article  Google Scholar 

  27. https://github.com/pjreddie/darknet/blob/master/data/coco.names

  28. Jiang F, Salama P, King B (2017) “A Public-Key Approach of Selective Encryption for Images”. IJ Network Security 19(1):118–126

    Google Scholar 

  29. Kanáliková A, Franeková M, Bubeníková E (2019) Trends in the area of security within c2c communications. Annals of the faculty of engineering hunedoara-international journal of engineering, 17.1 pp.181–188

  30. Lowe DG (1999) “Object recognition from local scale-invariant features.” iccv. Vol. 99 No. 2 pp.1150–1157

  31. Maroti D, Nain N, Ahmed M (2018) “Efficient and secure multi secret sharing schemes based on boolean XOR and arithmetic modulo”. Multimedia Tools and Applications 77(1):89–107

    Article  Google Scholar 

  32. Ovshinsky SR, Pashmakov B (2004) “Methods of factoring and modular arithmetic.” U.S Patent No. 6,714,954. 30 Mar.

  33. Papageorgiou CP, Oren M, Poggio T (1998) “A general framework for object detection.” Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271). IEEE

  34. Rajput M, et al. (2018) Securing data through steganography and secret sharing schemes: Trapping and misleading potential attackers. IEEE Consumer Electronics Magazine 7(5):40–45

    Article  Google Scholar 

  35. Redmon J, et al. (2016) “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition

  36. Sermanet P, et al. (2013) “Overfeat: Integrated recognition, localization and detection using convolutional networks”. arXiv:1312.6229

  37. Shaoqing R, et al. (2015) “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems

  38. Shaoqing R, et al. (2015) “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems

  39. Shyu SJ (2006) “Efficient visual secret sharing scheme for color images”. Pattern Recognition 39(5):866–880

    Article  Google Scholar 

  40. Simmons GJ (1979) “Symmetric and asymmetric encryption”. ACM Computing Surveys (CSUR) 11(4):305–330

    Article  Google Scholar 

  41. Stallings W (2006) Cryptography and network security, 4/E Pearson Education India

  42. Szegedy C, Toshev A, Erhan D (2013) “Deep neural networks for object detection.” Advances in neural information processing systems

  43. Szegedy C, et al. (2014) “Scalable, high-quality object detection”. arXiv:1412.1441

  44. Tanabe Y, Ishida T (2017) “Quantification of the accuracy limits of image registration using peak signal-to-noise ratio”. Radiological physics and technology 10 (1):91–94

    Article  Google Scholar 

  45. Uijlings JRR, et al. (2013) “Selective search for object recognition”. International journal of computer vision 104(2):154–171

    Article  Google Scholar 

  46. Yang C-N, Chen C-H, Cai S-R (2016) “Enhanced Boolean-based multi secret image sharing scheme”. Journal of Systems and software 116:22–34

    Article  Google Scholar 

Download references

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Correspondence to Maroti Deshmukh.

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Agarwal, A., Deshmukh, M. & Singh, M. Object detection framework to generate secret shares. Multimed Tools Appl 79, 24685–24706 (2020). https://doi.org/10.1007/s11042-020-09169-x

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  • DOI: https://doi.org/10.1007/s11042-020-09169-x

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