Detection of Cyber Extremism Objects Through Utilizing Different Versions of YOLOv8 Model to Determine their Performance.
In this paper, we compare various YOLOv8 model variants on a custom-made dataset containing cyber extremism images to analyze their performance in detecting cyber-extremism content images. The experiment discussed was performed on 3 specific models: YOLOv8s, YOLOv8m, and YOLOv8x; the models were tra...
| Publicado en: | Journal of Human Security (Librello Publishing House) Vol. 20; no. 2; pp. 9 - 16 |
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| Autores principales: | , |
| Formato: | Artículo |
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Librello Publishing House
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187722730&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187722730 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 18353800 ESYK jtl: Journal of Human Security (Librello Publishing House) issn: 18353800 maglogo: N pubinfo: dt: 2024 vid: 20 iid: 2 pid: 83753 pub: Librello Publishing House artinfo: ui: 187722730 10.12924/johs2024.20202 ppf: 9 ppct: 7 formats: fmt: @attributes: type: P size: 1.4MB tig: atl: Detection of Cyber Extremism Objects Through Utilizing Different Versions of YOLOv8 Model to Determine their Performance. aug: au: Berznji, Ala Amin, Shvan A. Mohamed affil: Computer Department, College of Science, University of Sulaimani, Sulaymaniyah 46001, Iraq Stockholm University, Department of Computer and System Sciences Cyber Security Department, IQ Group Holding, Sulaymaniyah 46001, Iraq su: Radicalism Ethical problems Military vehicles System identification Cyberspace sug: subj: Radicalism Transportation Equipment and Supplies (except Motor Vehicle) Merchant Wholesalers Ethical problems Military vehicles System identification Cyberspace keyword: Cyber Extremism Models Object Detection YOLOv8 Cyber Extremism Models Object Detection YOLOv8 ab: In this paper, we compare various YOLOv8 model variants on a custom-made dataset containing cyber extremism images to analyze their performance in detecting cyber-extremism content images. The experiment discussed was performed on 3 specific models: YOLOv8s, YOLOv8m, and YOLOv8x; the models were trained and assessed in the same conditions for a fair comparison. The images used for testing included visual elements that pertain to extremism, such as weapons, explosions, military vehicles, symbols, and ruined infrastructures. Manually collected a dataset of 1955 images containing 1126 labeled objects in 12 classes from open-source platforms. For the annotation of the TEGEMO dataset, the CVAT tool was employed, and bounding boxes were utilized for object detection and labeling. The models were trained on Google Colab of an A100 GPU, and performance was measured using standard object detection metrics like P, R, and mAP (mean Average Precision). According to the results, all YOLOv8 models were accurate in the detection of cyber extremism-related objects. Overall, YOLOv8x obtained the best mAP@50 score with 0.987, but YOLOv8s is the fastest in term of inference time, making it the compose to real-time applications. YOLOv8m was selected as the best compromise between accuracy and speed. The results validate the potential of the YOLOv8 models to aid the development of automated detection systems for the identification of extremist visual media. The study also discusses the limitations, including the small size of the dataset which varies in terms of extremist content, the problems of ethical considerations of using AI for surveillance, etc. Further work will involve the enrichment of datasets, preparation against adversarial attacks, and the employment of NLP for the discovery of violent content in a"multi-modal" context. This research serves as groundwork for the development of AI driven systems for combating cyber extremism, promoting cyberspace safety via intelligent visual content analysis. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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