Remote medical video region tamper detection system based on Wireless Sensor Network
Keywords:Wireless sensor network, Telemedicine, Video area, Tampering detection system, Sensor nodes, Suspicious movement point
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.
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
How to Cite
Copyright (c) 2022 EAI Endorsed Transactions on Pervasive Health and Technology
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.