Lehmer Algoritması Tabanlı Rastgele Anahtar İle Şifrelenmiş Görüntüleri Kullanarak Nesne Tespitli Doğrulama Yoluyla Güvenli Mesaj İletimi

Abstract

Günümüzde son kullanıcılar arası verinin başarılı iletimi, verinin korunması ve gizlilik önemli konulardır. Çalışmamızda, uçtan uca (end-to-end) güvenilir bir iletişim kanalı üzerinden rastgele anahtar üretimi için Lehmer algoritması tabanlı piksel renk matris bilgilerinde bit bazında şifrelenmiş görüntülerin iletilmesini sağlayan ve doğruluk geçerlemesini garanti eden bir sistem oluşturulmuştur. Derin öğrenme modeli kullanılarak görüntü sahnesi içerisindeki tespit edilen nesnelerin özellikleri ile üst veri oluşturulmaktadır. Çalışmada, mesajın göndericisi tarafından Lehmer algoritması kullanılarak şifreleme yapıldıktan sonra Base64 kodlama formatında yapılandırılmış veri oluşturulmaktadır. Güvenli iletim kanalı üzerinden bu mesaj alıcı tarafa uçtan uca iletir. Son olarak alıcı tarafa ulaşan mesaj paketindeki görüntü sahnesi içerisindeki nesnelerin tespit bilgilerini içeren yapılandırılmış veri değişim ve aktarım dosyasından görüntünün kodlanmış hali ve üst veri bilgileri ayıklanarak şifre çözümlemesi yapılmaktadır. Alıcı tarafta derin öğrenme modelinin nesne tespit sonuçları bu dosyadan elde edilen deşifrelenmiş bilgi ile kıyaslanarak mesajın doğruluğunun geçerlemesi şeklinde nesnel sağlaması da yapılmaktadır. Yapılan deneylerdeki ortalama değerler göz önüne alındığında üç farklı veri kümesinden elde edilen veriler için ortalama değerler olarak Piksel Sayısı Değişim Hızı (NPCR) %99,61, Birleşik Ortalama Değişme Yoğunluğu (UACI) %14,70 ve ortalama entropi değeri ise şifrelenmiş görüntüler için azami 7,9999 değerinde ölçülmüştür. Çalışmamızda bilimsel bulgulara dayanan tartışma ve değerlendirmelere yer verilmektedir.

Keywords

• Rastgele sayı
• şifreleme
• derin öğrenme
• veri iletişimi

Secure Message Transmission Via Object Detection Verification Using Images Encoded With Lehmer Algorithm Based Random Key

Abstract

Nowadays, the successful transmission of data between end users, data protection and confidentiality are important issues. In our study, a system that ensures the transmission of bitwise encrypted images in pixel color matrix information based on the Lehmer algorithm for the generation of random keys over an end-to-end reliable communication channel and guarantees accuracy validation has been created. Using the deep learning model, metadata is created with the properties of the detected objects in the image scene. In the study, structured data is created in Base64 encoding format after encryption is done by the sender end of the message using the Lehmer algorithm. This message is transmitted end-to-end to the receiving party over the secure transmission channel. Finally, the encoded version of the image and the metadata information are extracted from the structured data exchange and transfer file containing the detection information of the objects in the image scene in the message packet reaching the receiver end, and also, decryption is performed. On the receiver end, object detection results of the deep learning model are compared with the decrypted information obtained from this file, and an objective verification of the accuracy of the message is also performed. Considering the average values in the experiments, the average values with the data obtained from three different data sets are for the Number of Pixels Change Rate (NPCR) 99.61%, the Unified Average Changing Intensity (UACI) 14.70%, and also, the maximum average entropy value is obtained 7.9999 for encrypted images. In our study, discussions and evaluations based on scientific findings are included.

Keywords

• Random number
• encryption
• deep learning
• data communication

References

1. N. Koo, G. H. Cho, Byeonghwan and S. Kwon, “An Improvement of the Cipolla-Lehmer Type Algorithms,” 2015, National Institute for Mathematical Sciences, arXiv Preprint: 1501.04036 [cs.CR], Available at: https://doi.org/10.48550/arXiv.1501.04036 [Accessed: Aug., 2022].
2. D. Saif and A. Matrawy, "A Pure HTTP/3 Alternative to MQTT-over-QUIC in Resource-Constrained IoT," 2021, Carleton University, arXiv Preprint: 2106.12684 [cs.NI], Available at: https://doi.org/10.48550/arXiv.2106.12684. [Accessed: Aug., 2022].
3. M. Ahmed and M. M. Akhtar, "Smart Home: Application using HTTP and MQTT as Communication Protocols," 2021, Indian Institute of Technology, Delhi, arXiv Preprint: 2021.10339 [cs.CR], Available at: https://doi.org/10.48550/arXiv.2112.10339. [Accessed: Aug., 2022].
4. G. Perrone, M. Vecchio, J. D. Ser, F. Antonelli, V. Kapoor, "The Internet of Things: a Survey and Outlook," 2019, Research Centre at eCampus University, Novedrate (Como), Italy, arXiv Preprint:1910.13965 [cs.NI], Available at: https://doi.org/10.48550/arXiv.1910.13965. [Accessed: Aug., 2022].
5. M. Lirzin and B. Markhoff, "Towards an ontology of HTTP interactions," 2020, France, arXiv Preprint: 2007.13475 [cs.AI], Available at: https://doi.org/10.48550/arXiv.2007.13475. [Accessed: Aug., 2022].
6. S. Hazelhurst, "A Proposal for Dynamic Access Lists for TCP/IP Packet Filering," 2001, Programme for Highly Dependable Systems, arXiv Preprint: cs/0110013 [cs.NI], Available at: https://doi.org/10.48550/arXiv.cs/0110013. [Accessed: Aug., 2022].
7. S. Kumar, M. P. Andersen, H.-S. Kim and D. E. Culler, "Performant TCP for Low-Power Wireless Networks," 2018, University of California, Berkeley, arXiv Preprint: 1811.02721 [cs.NI], Available at: https://doi.org/10.48550/arXiv.1811.02721.[Accessed: Aug., 2022].
8. Y. Bengio, A. C. Courville and P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, Aug 2013, doi: 10.1109/TPAMI.2013.50.

9. P. Jiang, D. Ergu, F. Liu, Y. Cai and B. Ma, "Review of Yolo Algorithm Developments", Procedia Computer Science, vol. 199, no. 1, pp. 1066-1073, 2022, doi: 10.1016/j.procs.2022.01.135. ISSN 1877-0509.
10. A. Radford, J.-W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger and I. Sutskever, "Learning Transferable Visual Models From Natural Language Supervision," 2021, arXiv Preprint: 2103.00020 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2103.00020. [Accessed: Aug., 2022].
11. A.K. Banthia and N. Tiwari, “Image Encryption Using Pseudo Random Number Generators,” International Journal of Computer Applications, vol. 67, no. 20, pp. 1-8, April 2013.
12. V. Viswanatha, R. K. Chandana, A. C. Ramachandra, “Real Time Object Detection System with YOLO and CNN Models: A Review,” 2022, arXiv Preprint: 2208.00773 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2208.00773. [Accessed: Aug., 2022].
13. H. Oğraş and M. R. Tür, "An Effective Image Encryption Algorithm Using Bit Reversal Permutation and a New Chaotic Map," Gazi University Journal of Science, vol. 35, no. 2, pp. 542-556, Jun. 2022. doi: 10.35378/gujs.872818.
14. S. Somaraj and M.A. Hussain, "Securing Medical Images by Image Encryption using Key Image," International Journal of Computer Applications, vol. 104, no. 3, pp. 30-34, October 2014. doi: 10.5120/18184-9079.
15. S.-W. Kang, and U.-S., Choi, "ROI Image Encryption using YOLO and Chaotic Systems," International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, pp. 466-474, 2021. doi: 10.14569/IJACSA.2021.0120754.
16. P. Lorek, G. Los, K. Gotfryd, and F. Zagórski, “On testing pseudorandom generators via statistical tests based on the arcsine law,” 2019, arxiv Preprint: 1903.09805 [math.PR], Available at: https://doi.org/10.48550/arXiv.1903.09805. [Accessed: Aug., 2022].
17. K.S. Chua, "Chebyshev Polynomials And Higher Order Lucas Lehmer Algorithm," 2020, arXiv Preprint: 2010.02677 [math.NT], Available at: https://doi.org/10.48550/arXiv.2010.02677. [Accessed: Aug., 2022].
18. H.G. Katzgraber, “Random Numbers in Scientific Computing: An Introduction,” 2010, Oldenburg, Germany, arXiv Preprint: 1005.4117 [physics.comp-ph], Available at: https://doi.org/10.48550/arXiv.1005.4117. [Accessed: Aug., 2022].
19. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “ You Only Look Once: Unified, Real-Time Object Detection,” 2015, University of Washington, arXiv Preprint: 1506.02640 [cs.CV], Available at: https://doi.org/10.48550/arXiv.1506.02640. [Accessed: Aug., 2022].
20. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” 2014, Stanford University, Stanford, USA, arXiv Preprint: 1409.0575 [cs.CV], Available at: https://doi.org/10.48550/arXiv.1409.0575. [Accessed: Aug., 2022].
21. T. Ridnik, H. Lawen, E. Ben-Baruch, and A. Noy, "Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results," 2022, arXiv Preprint: 2204.03475 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2204.03475. [Accessed: Aug., 2022].
22. L. Beyer, O. J. Hénaff, A. Kolesnikov, X. Zhai, A.V.D. Oord, "Are we done with ImageNet?," 2020, Zurich, arXiv Preprint: 2006.07159 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2006.07159. [Accessed: Aug., 2022].
23. Z. Wang, W. Liu, Q. He, X. Wu, and Z. Yi, "CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP," 2022, arXiv Preprint:2203.00386 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2203.00386. [Accessed: Aug., 2022].
24. Y. Cui, L. Zhao, F. Liang, Y. Li, and J. Shao, "Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision," 2022, arXiv Preprint: 2203.05796 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2203.05796. [Accessed: Aug., 2022].
25. H. You, L. Zhou, B. Xiao, N. Codella, Y. Cheng, R. Xu, S.-F. Chang, and L. Yuan, "Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training," 2022, arXiv Preprint: 2207.12661 [cs.CV], Available at: https://doi.org/10.48550/arXiv.2207.12661. [Accessed: Aug., 2022].
26. Katna API Video module documentation website, 2022. [Online]. Available at: https://katna.readthedocs.io/en/latest/understanding_katna.html. [Accessed: Aug., 2022].
27. Python Imaging Library (PIL) Website, 2022. [Online]. Available at: https://python-pillow.org/. [Accessed: Aug., 2022].
28. Y. Shi and Z. Zhang, "Communication Complexities of XOR functions," Quantum Information & Computation, vol. 9, no. 3, pp. 255–263, March 2009. doi: 10.5555/2011781.2011786. ISSN: 1533-7146.
29. P. Bourhis, J. L. Reutter, F. Suárez and D. Vrgoc, "JSON: data model, query languages and schema specification," 2017, arXiv Preprint: 1701.02221 [cs.DB], Available at: https://doi.org/10.48550/arXiv.1701.02221. [Accessed: Aug., 2022].
30. Labeled Faces in the Wild data set website, 2022. [Online]. Available at: http://viswww.cs.umass.edu/lfw/. [Accessed: Aug., 2022].
31. E. Learned-Miller, G.B. Huang, A. R, Chowdhury, H. Li, and G. Hua, “Labeled Faces in the Wild: A Survey,” In Advances in Face Detection and Facial Image Analysis, Editörler: M. Kawulok, M. E. Celebi ve B. Smolka, Springer, Cham, pp. 189-248, 2016, doi: 10.1007/978-3-319-25958-1_8. ISBN: 978-3-319-25958-1.
32. M. Everingham, L.V. Gool, C.K.I. Williams, J. Winn and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, pp. 303–338, 2010. doi: 10.1007/s11263-009-0275-4.
33. Oxford Pascal VOC “Visual Object Classes Challenge 2012 (VOC2012)” data set website, 2022. [Online]. Available at: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/. [Accessed: Aug., 2022].
34. Tensorflow Deep Learning website, 2022. [Online]. Available at: https://www.tensorflow.org/datasets/catalog/voc. [Accessed: Aug., 2022].
35. CPSM “Unsegmented Sports News” (CP Sports Minute) data set website, 2022. [Online]. Available at: https://homepages.inf.ed.ac.uk/thospeda/downloads.html. [Accessed: Aug., 2022].
36. T. M. Hospedales, S. Gong and T. Xiang, "Learning Tags from Unsegmented Videos of Multiple Human Actions," 2011 IEEE 11th International Conference on Data Mining, pp. 251-259, 11-14 December 2011, https://doi.org/10.1109/ICDM.2011.90.
37. J. Nilsson, and T. Akenine-Möller, “Understanding SSIM,” 2020, arXiv Preprint: arXiv:2006.13846 [eess.IV], Available at: https://doi.org/10.48550/arXiv.2006.13846. [Accessed: Aug., 2022].
38. O. Keleş, M.A. Yılmaz, A.M. Tekalp, C. Korkmaz, and Z. Doğan, “On the Computation of PSNR for a Set of Images or Video,” 2021, arXiv Preprint: arXiv:2104.14868 [eess.IV], Available at: https://doi.org/10.48550/arXiv.2104.14868. [Accessed: Aug., 2022].
39. A.A. Shah, A. Adeel, J. Ahmad, A. Al-Dubai, M. Gogate, A. Bishnu, M. Diyan, T. Hussain, K. Dashtipour, T. Ratnarajah, and A. Hussain, "A Novel Chaos-based Light-weight Image Encryption Scheme for Multi-modal Hearing Aids," 2022, arXiv Preprint: 2202.05662 [cs.CR], Available at: https://doi.org/10.48550/arXiv.2202.05662. [Accessed: Aug., 2022].
40. Y. Wu, J.P. Noonan and S. Agaian, "NPCR and UACI Randomness Tests for Image Encryption," Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), vol. (April 2011 Edition), pp.31-38. 2011. ISSN: 1925-2676.
41. D. J.C. MacKay, "Information Theory, Inference, and Learning Algorithms," Cambridge University Press. UK, 2003, 628 pages, ISBN:9780521642989
42. N.K. Pareek, V. Patidar, and K.K. Sud, "Colour Image Encryption Scheme Based on Permutation and Substitution Techniques," In Advances in Computer Science and Information Technology, CCSIT 2011, Communications in Computer and Information Science, Part I, Editörler: N. Meghanathan, B.K. Kaushik, D. Nagamalai, Springer, Berlin, Heidelberg, vol. 131, pp. 413-327, 2011, doi: 10.1007/978-3-642-17857-3_41. ISBN: 978-3-642-17856-6.
43. S. Zhu, G. Wang, and C. Zhu, “A Secure and Fast Image Encryption Scheme based on Double Chaotic S-Boxes,” Entropy, vol. 21, no. 8, pp. 790 (1-20), Aug. 2019. doi: 10.3390/e21080790.
44. Ü. Çavuşoğlu, S. Kaçar, I. Pehlivan, and A. Zengin, "Secure image encryption algorithm design using a novel chaos based S-box," Chaos Solitons Fractals, vol. 95, pp. 92–101, 2017. doi: 10.1016/j.chaos.2016.12.018.
45. M. Hanif, N. Iqbal, F.U. Rahman, M.A. Khan, T.M. Ghazal, S. Abbas, M. Ahmad, H.A. Hamadi, and C.Y. Yeun, “A Novel Grayscale Image Encryption Scheme Based on the Block-Level Swapping of Pixels and the Chaotic System,” Sensors, vol. 22, no. 16, pp. 6243 (1-19), Aug. 2022. doi: 10.3390/s22166243.