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.
Similar content being viewed by others
References
Arraziqi D, Haq ES (2019) “Optimization of video steganography with additional compression and encryption”. TELKOMNIKA 17(3):1417–1424
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
Brodal GS, Moruz G, Stølting G (2006) “Skewed binary search trees”. European Symposium on Algorithms, Springer, Berlin Heidelberg
Carlson JA (2017) “Method for secure communication using asymmetric and symmetric encryption over insecure communications.” U.S Patent No. 9,819,656. 14 Nov.
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
Chen T-H, Wu C-S (2011) “Efficient multi-secret image sharing based on Boolean operations”. Signal Processing 91(1):90–97
Cox I, et al. (2007) Digital watermarking and steganography Morgan kaufmann
COCO Dataset: http://cocodataset.org/
Dai J, et al. (2016) “R-fcn: Object detection via region-based fully convolutional networks.” Advances in neural information processing systems
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
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
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
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
Deshmukh M, Nain N, Ahmed M (2018) “Secret sharing scheme based on binary trees and Boolean operation.” Knowledge and Information Systems:, vol 60
Di PR, Guarino S (2013) “Data confidentiality and availability via secret sharing and node mobility in UWSN.” 2013 Proceedings IEEE INFOCOM IEEE
Donahue J, et al. (2014) “Decaf: A deep convolutional activation feature for generic visual recognition.” International conference on machine learning
Dong J, et al. (2014) “Towards unified object detection and semantic segmentation,” European Conference on Computer Vision Springer, Cham
Duseja T, Deshmukh M (2019) “Image compression and encryption using chinese remainder theorem.” Multimedia Tools and Applications:, vol 78
Erhan D, et al. (2014) “Scalable object detection using deep neural networks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition
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
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
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
Feng J-B, et al. (2005) A new multi-secret images sharing scheme using Largranges interpolation. J Syst Softw 76(3):327–339
Forouzan BA (2007) Cryptography & network security. McGraw-Hill Inc.
Girshick R (2015) “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision
Guo T, Liu F, Wu C (2014) “K out of k extended visual cryptography scheme by random grids”. Signal Processing 94:90–101
https://github.com/pjreddie/darknet/blob/master/data/coco.names
Jiang F, Salama P, King B (2017) “A Public-Key Approach of Selective Encryption for Images”. IJ Network Security 19(1):118–126
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
Lowe DG (1999) “Object recognition from local scale-invariant features.” iccv. Vol. 99 No. 2 pp.1150–1157
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
Ovshinsky SR, Pashmakov B (2004) “Methods of factoring and modular arithmetic.” U.S Patent No. 6,714,954. 30 Mar.
Papageorgiou CP, Oren M, Poggio T (1998) “A general framework for object detection.” Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271). IEEE
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
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
Sermanet P, et al. (2013) “Overfeat: Integrated recognition, localization and detection using convolutional networks”. arXiv:1312.6229
Shaoqing R, et al. (2015) “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems
Shaoqing R, et al. (2015) “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems
Shyu SJ (2006) “Efficient visual secret sharing scheme for color images”. Pattern Recognition 39(5):866–880
Simmons GJ (1979) “Symmetric and asymmetric encryption”. ACM Computing Surveys (CSUR) 11(4):305–330
Stallings W (2006) Cryptography and network security, 4/E Pearson Education India
Szegedy C, Toshev A, Erhan D (2013) “Deep neural networks for object detection.” Advances in neural information processing systems
Szegedy C, et al. (2014) “Scalable, high-quality object detection”. arXiv:1412.1441
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
Uijlings JRR, et al. (2013) “Selective search for object recognition”. International journal of computer vision 104(2):154–171
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
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09169-x