Geo-Tagged Spoofing Detection using Jaccard Similarity
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...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2023-10-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/4239 |
| _version_ | 1797648674358034432 |
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| author | Shweta Koparde Vanita Mane |
| author_facet | Shweta Koparde Vanita Mane |
| author_sort | Shweta Koparde |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-11T15:35:21Z |
| format | Article |
| id | doaj.art-a968511735e141c8a56c60b7c003d4dc |
| institution | Directory Open Access Journal |
| issn | 2032-944X |
| language | English |
| last_indexed | 2024-03-11T15:35:21Z |
| publishDate | 2023-10-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj.art-a968511735e141c8a56c60b7c003d4dc2023-10-26T18:34:48ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2023-10-011010.4108/ew.4239Geo-Tagged Spoofing Detection using Jaccard SimilarityShweta Koparde0Vanita Mane1D.Y. Patil University D.Y. Patil University 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. https://publications.eai.eu/index.php/ew/article/view/4239Spoofing detectionDicerete Cosine TransformTanimoto similarityFuzzy filter |
| spellingShingle | Shweta Koparde Vanita Mane Geo-Tagged Spoofing Detection using Jaccard Similarity EAI Endorsed Transactions on Energy Web Spoofing detection Dicerete Cosine Transform Tanimoto similarity Fuzzy filter |
| title | Geo-Tagged Spoofing Detection using Jaccard Similarity |
| title_full | Geo-Tagged Spoofing Detection using Jaccard Similarity |
| title_fullStr | Geo-Tagged Spoofing Detection using Jaccard Similarity |
| title_full_unstemmed | Geo-Tagged Spoofing Detection using Jaccard Similarity |
| title_short | Geo-Tagged Spoofing Detection using Jaccard Similarity |
| title_sort | geo tagged spoofing detection using jaccard similarity |
| topic | Spoofing detection Dicerete Cosine Transform Tanimoto similarity Fuzzy filter |
| url | https://publications.eai.eu/index.php/ew/article/view/4239 |
| work_keys_str_mv | AT shwetakoparde geotaggedspoofingdetectionusingjaccardsimilarity AT vanitamane geotaggedspoofingdetectionusingjaccardsimilarity |