Remote medical video region tamper detection system based on Wireless Sensor Network

Authors

  • Sujuan Li College of Electronic Information Engineering, Hebi Polytechnic,Hebi 458030,China
  • Shichen Huang College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China

DOI:

https://doi.org/10.4108/eetpht.v8i31.702

Keywords:

Wireless sensor network, Telemedicine, Video area, Tampering detection system, Sensor nodes, Suspicious movement point

Abstract

INTRODUCTION: A new telemedicine video tamper detection system based on wireless sensor network is proposed and designed in this paper.

OBJECTIVES: This work is proposed to improve the performance of telemedicine video communication and accurately detect the tamper area in telemedicine video.

METHODS: The sensor nodes in the sensing layer are responsible for collecting telemedicine video information and transmitting the information to the data layer. The data layer completes the storage of information and transmits it to the processing layer. The detection module of the processing layer detects the tampered area of the telemedicine video through two parts: suspicious moving point calculation and tamper detection, and transmits the detection results to the application display layer for display.

RESULTS: The experimental results show that the designed detection system can accurately detect the tampered area in the telemedicine video, and the packet loss rate is significantly reduced, and the maximum packet loss rate is no more than 1%.

CONCLUSION: The proposed detection system for remote medical video based on wireless sensor network can better meet the requirements of region tamper detection.

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References

Liu X, Chen S, Song L, et al (2021). Self-attention Negative Feedback Network for Real-time Image Super-Resolution, Journal of King Saud University - Computer and Information Sciences, online first, 10.1016/j.jksuci.2021.07.014 DOI: https://doi.org/10.1016/j.jksuci.2021.07.014

Liu S, Wang S, Liu X, et al (2022). Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring, IEEE Internet of Things Journal, online first, 10.1109/JIOT.2022.3142115 DOI: https://doi.org/10.1109/JIOT.2022.3142115

Vinodha, D. , Anita, E. M. & Geetha, D. M. (2021). A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (nmfmp-cda). Wireless Networks, 27(2), 1111-1128. DOI: https://doi.org/10.1007/s11276-020-02499-6

Singh, P. & Mittal, N. (2021). An efficient localization approach to locate sensor nodes in 3d wireless sensor networks using adaptive flower pollination algorithm. Wireless Networks, 27(3), 1999-2014. DOI: https://doi.org/10.1007/s11276-021-02557-7

Khalid H , Hashim S J , Ahmad S , Hashim F , Chaudhary M A . (2020). Cross-SN: A Lightweight Authentication Scheme for a Multi-Server Platform Using IoT-Based Wireless Medical Sensor Network. Electronics, 10(7):790-798. DOI: https://doi.org/10.3390/electronics10070790

Gowda, C. S. & Jayasree, P. (2021). Rendezvous points based energy-aware routing using hybrid neural network for mobile sink in wireless sensor networks. Wireless Networks, 27(4), 2961-2976. DOI: https://doi.org/10.1007/s11276-021-02630-1

Kim Tae Hyung, Park Cheol Woo, Eom Il Kyu. (2022). Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual[J]. Symmetry, 14(2):364-371. DOI: https://doi.org/10.3390/sym14020364

Ghamsarian, N. , Schoeffmann, K. & Khademi, M. (2021). Blind mv-based video steganalysis based on joint inter-frame and intra-frame statistics. Multimedia Tools and Applications, 80(6), 9137-9159. DOI: https://doi.org/10.1007/s11042-020-10001-9

Liu S, Guo C, Fadi A, et al (2020). Reliability of Response Region: A Novel Mechanism in Visual Tracking by Edge Computing for IIoT Environments, Mechanical Systems and Signal Processing, 138, 106537 DOI: https://doi.org/10.1016/j.ymssp.2019.106537

Li, X. H. & Wang, X. X. (2021). An image Tamper Detection Method for CNN Mobile Devices Based on Pruning Compression. Computer Simulation, 38(03), 83-86+91.

Gharbi, W. , Chaari, L. & Benazza-Benyahia, A. (2021). Unsupervised bayesian change detection for remotely sensed images. Signal, Image and Video Processing, 15(1), 205-213. DOI: https://doi.org/10.1007/s11760-020-01738-9

Yan Pu, Su Liangliang, Shaohui, Wu Dongsheng. (2019). Image forgery detection based on local intensity order and multi-support region. Journal of Computer Applications, 39(09):2707-2711.

Liu S, Liu D, Gautam S, et al (2021). Overview and methods of correlation filter algorithms in object tracking. Complex & Intelligent Systems, 7: 1895-1917. DOI: https://doi.org/10.1007/s40747-020-00161-4

Kociszewski R . Implementation of PI Controller in Reconfigurable PSoC Microcontroller to Control the Speed of Mobile Robot Drives[C]// 15th International Conference Mechatronic Systems and Materials (MSM2020). 2020. DOI: https://doi.org/10.1109/MSM49833.2020.9202384

Narasimhamurthy S , Danilov N , Wu S , et al. (2019). SAGE: Percipient Storage for Exascale Data Centric Computing. Parallel Computing, 83(14):22-33. DOI: https://doi.org/10.1016/j.parco.2018.03.002

Li Shao, Huang Cheng. (2020). Simulation of Distributed Big Data Intelligent Storage Algorithm under Cloud Computing. Computer Simulation, 37(05):443-447.

Singhal C , Patil V . (2021). HCR-WSN: Hybrid MIMO cognitive radio system for wireless sensor network. Computer Communications, 169(1):11-25. DOI: https://doi.org/10.1016/j.comcom.2020.12.025

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Published

26-07-2022

How to Cite

1.
Li S, Huang S. Remote medical video region tamper detection system based on Wireless Sensor Network. EAI Endorsed Trans Perv Health Tech [Internet]. 2022 Jul. 26 [cited 2024 Mar. 29];8(31):e3. Available from: https://publications.eai.eu/index.php/phat/article/view/702