Geo-Tagged Spoofing Detection using Jaccard Similarity
DOI:
https://doi.org/10.4108/ew.4239Keywords:
Spoofing detection, Dicerete Cosine Transform, Tanimoto similarity, Fuzzy filterAbstract
In recent years, position evaluation of mobile devices has developed as an essential part of social movement. Meantime, the criminals may interfere with the information of geographical position (geo-position), and they can adjust the geo-position for their convenience. Therefore, it is important to identify the authenticity of geo-position. In this paper, an instant messaging platform-based geo-tagged spoof image detection system is created using Jaccard similarity. With the help of a Fuzzy filter, the input, as well as spoofing images, are subjected to camera footprint extraction, and their corresponding outputs are fused by Dice Coefficient. Moreover, the input as well as spoofed images is subjected to geotagged process, and their corresponding geotagged input, and geotagged spoofed images are fused by Tanimoto similarity. At last, the fused images from Dice Coefficient, and Tanimoto similarity are employed for the spoof detection process, where the Jaccard similarity compares the two images using Dicerete Cosine Transform (DCT). Consequently, the spoofed images are detected, and their effectiveness is measured in terms of accuracy, False Positive Rate (FPR), and True Positive Rate (TPR), as well as the corresponding values are attained like 0.099, 0.892, and 0.896 respectively.
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Vinolin V, Sucharitha M, Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment. The Visual Computer 2021, 37(8), 2369-2390. DOI: https://doi.org/10.1007/s00371-020-01992-5
Taneja A, Tayal A, Malhorta A, Sankaran A, Vatsa M, Singh R,: Fingerphoto spoofing in mobile devices: a preliminary study. In proceedings of IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) 2016, 1-7. DOI: https://doi.org/10.1109/BTAS.2016.7791201
Koh J.Y, Nevat I., and Wong L.: Geo-Spatial Location Spoofing Detection for Internet of Things. IEEE Internet of Things Journal 2016. DOI: https://doi.org/10.1109/JIOT.2016.2535165
Zhou W, Lv Z, Deng X, and Ke Y.: A New Induced GNSS Spoofing Detection Method Based on Weighted Second-Order Central Moment. IEEE Sensors Journal 2022, 22(12), 12064-12078. DOI: https://doi.org/10.1109/JSEN.2022.3174019
Jayan T.J, Aneesh R.P: Image quality measures-based face spoofing detection algorithm for online social media. In proceedings of 2018 International CET Conference on Control, Communication, and Computing (IC4), 2018, 245-249. DOI: https://doi.org/10.1109/CETIC4.2018.8531037
Zheng Q.Y, Geng L, Ni M, Sun J.Y, Ren P, Ji Q.B, Li J.C, Zhang G.Q: Modern instant messaging platform for postoperative follow-up of patients after total joint arthroplasty may reduce re-admission rate. Journal of Orthopaedic Surgery and Research 2019. 14, 1-9. DOI: https://doi.org/10.1186/s13018-019-1407-3
Huang Q, Liu Y. On geo-social network services. In proceeding of 2009 17th International Conference on Geoinformatics, 2009, 1-6.
Lee I, Cai G, Lee K.: Exploration of geo-tagged photos through data mining approaches. Expert Systems with Applications 2014, 41(2), 397-405. DOI: https://doi.org/10.1016/j.eswa.2013.07.065
Bao J, Zheng Y, Wilkie D, Mokbel M.: Recommendations in location-based social networks: a survey. Geo Informatica 2015, 19, 525-565. DOI: https://doi.org/10.1007/s10707-014-0220-8
Compton R, Lee C, Lu T.C, De Silva L, Macy M.: Detecting future social unrest in unprocessed twitter data:“emerging phenomena and big data”. In procedings of 2013 IEEE International Conference on Intelligence and Security Informatics 2013, 56-60. DOI: https://doi.org/10.1109/ISI.2013.6578786
Deligiannis N, Huu T.D, Nguyen D.M, Luo X.: Deep learning for geolocating social media users and detecting fake news. In NATO Workshop 2018.
Varshosaz M, Afary A., Mojaradi B, Saadatseresht M, and Parmehr E.G. Spoofing Detection of Civilian UAVs Using Visual Odometry. International Journal of Geo-Information 2020, 9(1). DOI: https://doi.org/10.3390/ijgi9010006
Gao Y, and Li G. A Slowly Varying Spoofing Algorithm Avoiding Tightly-Coupled GNSS/IMU With Multiple Anti-Spoofing Techniques. IEEE Transactions on Vehicular Technology 2022, 71(8). DOI: https://doi.org/10.1109/TVT.2022.3174406
Qiao T, Zhao Q, Zheng N, Xu M, Zhang L.: Geographical position spoofing detection based on camera sensor fingerprint. Journal of Visual Communication and Image Representation 2021, 81, 103320. DOI: https://doi.org/10.1016/j.jvcir.2021.103320
Ke Y, Lv Z, Zhang C, Deng X, Zhou W, and Song D. Tightly Coupled GNSS/INS Integration Spoofing Detection Algorithm Based on Innovation Rate Optimization and Robust Estimation. IEEE Access 2022,10, 72444-72457. DOI: https://doi.org/10.1109/ACCESS.2022.3186305
Gao Y. and Li G.: Two Time Spoofing Algorithms on GNSS Receiver Instrumentation of Modifying Satellite Clock Correction Parameters in Navigation Message. IEEE Transactions on Instrumentation and Measurement 2023, 72. DOI: https://doi.org/10.1109/TIM.2023.3241982
Sun X, Wu P, Hoi S.C. Face detection using deep learning: An improved faster RCNN approach. Neuro computing 2018, 299, 42-50. DOI: https://doi.org/10.1016/j.neucom.2018.03.030
Jeong S, Kim M, Lee J.: CUSUM-based GNSS Spoofing Detection Method for Users of GNSS Augmentation System. International Journal of Aeronautical and Space Sciences 2020, 21(2), 513-523. DOI: https://doi.org/10.1007/s42405-020-00272-9
Liu Y, Wang J, Niu S, Song H.: Deep learning enabled reliable identity verification and spoofing detection. In proceeding of International Conference on Wireless Algorithms, Systems, and Applications, 2020, 333-345. DOI: https://doi.org/10.1007/978-3-030-59016-1_28
Fadl S, Han Q, Li Q.: CNN spatiotemporal features and fusion for surveillance video forgery detection. Signal Processing: Image Communication2021, 90, 116066. DOI: https://doi.org/10.1016/j.image.2020.116066
Kwan H.K. Fuzzy filters for noisy image filtering. In Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS'03 4, IV-IV 2003).
Cha S.H.:Comprehensive survey on distance/similarity measures between probability density functions. City 2007, 1(2), 1.
European Cities 1M dataset available at, http://image.ntua.gr/iva/datasets/ec1m/index.html accessed on December 2022
Aditya, B.L.V.S., Rajaram, G., Hole, S.R., Mohanty, S.N. (2023). F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets. In: Nandan Mohanty, S., Garcia Diaz, V., Satish Kumar, G.A.E. (eds) Intelligent Systems and Machine Learning. ICISML 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-031-35081-8_2 DOI: https://doi.org/10.1007/978-3-031-35081-8_2
Mangla, M., Sayyad, A., Shama, N., Mohanty, S.N., Singh, D. (2023). An Artificial Intelligence and Computer Vision Based EyeWriter. In: Swarnkar, T., Patnaik, S., Mitra, P., Misra, S., Mishra, M. (eds) Ambient Intelligence in Health Care. Smart Innovation, Systems and Technologies, vol 317. Springer, Singapore. https://doi.org/10.1007/978-981-19-6068-0_43 DOI: https://doi.org/10.1007/978-981-19-6068-0_43
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