Self-Supervised Representation Learning for Criminology: Detecting Anomalies, Classifying Reports, and Mapping Networks

Noorul Hassan S.; Sivalakshmi S.; Janani M.; Fouziya A.; Thirisha S.

doi:10.51903/jtie.v5i1.473

Authors

Noorul Hassan S. Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India https://orcid.org/0009-0009-1095-5621
Sivalakshmi S. Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India https://orcid.org/0009-0000-7719-7241
Janani M. Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India https://orcid.org/0009-0002-0276-9107
Fouziya A. Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India
Thirisha S. Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India https://orcid.org/0009-0009-1690-3857

DOI:

https://doi.org/10.51903/jtie.v5i1.473

Keywords:

Self-Supervised Learning, Multimodal Analysis, Anomaly Detection, Crime Prediction, Graph Representation

Abstract

Crime analysis using various types of data, such as video surveillance, crime reports, and criminal networks, has been widely investigated in digital criminology. Most of the available data are unlabelled. In this work, we introduce a self-supervised learning framework for multimodal criminology, which enables the fully automatic learning of effective features for unlabelled video, text, and graph datasets and the completion of crime analysis tasks, including anomaly detection, crime report classification, and high-risk node prediction via contrastive learning, masked prediction, and graph self-supervised learning. The experimental results show that our SSL model learns high-quality features and achieves better performance than its supervised counterpart and baseline models. Unlike traditional deep learning-based models that require large amounts of labeled data, our proposed SSL model is label-efficient, scalable, and robust to artificial or anonymous data. Our work aims to develop an AI-based multimodal self-supervised learning approach for efficient, accurate, reliable, and safe crime analysis

References

Akshitha B. R., R. P., Chithra Shree G. C., A., & P. B., D. (2025). Real-Time Crime Insights: Anomaly Detection Using Machine Learning. IJARCCE, 14(11), 404–411. https://doi.org/10.17148/ijarcce.2025.141162

Bifari, E., Basbrain, A., Mirza, R., Bafail, A., Albaradei, S., & Alhalabi, W. (2024). Text Mining and Machine Learning for Crime Classification: Using Unstructured Narrative Court Documents in Police Academic. Cogent Engineering, 11(1), 2359850. https://doi.org/10.1080/23311916.2024.2359850

Birks, D., Groff, E. R., & Malleson, N. (2025). Agent-Based Modeling in Criminology. Annual Review of Criminology, 8(1), 75–95. https://doi.org/10.1146/annurev-criminol-022222-033905

Cavallaro, L., Ficara, A., Curreri, F., Fiumara, G., De Meo, P., Bagdasar, O., & Liotta, A. (2021). Graph Comparison and Artificial Models for Simulating Real Criminal Networks. In Complex Networks and Their Applications IX, 286–297. https://doi.org/10.1007/978-3-030-65351-4_23

Dakalbab, F., Abu Talib, M., Abu Waraga, O., Bou Nassif, A., Abbas, S., & Nasir, Q. (2022). Artificial Intelligence & Crime Prediction: A Systematic Literature Review. Social Sciences & Humanities Open, 6(1), 100342. https://doi.org/10.1016/j.ssaho.2022.100342

Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2025). CARLA: Self-Supervised Contrastive Representation Learning for Time Series Anomaly Detection. Pattern Recognition, 157, 110874. https://doi.org/10.1016/j.patcog.2024.110874

De Paula, D. D., Salvadeo, D. H. P., Silva, L. B., & Junior, U. P. (2023). Self-Supervised Feature Extraction for Video Surveillance Anomaly Detection. 2023 36th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 115–120. https://doi.org/10.1109/sibgrapi59091.2023.10347173

Dong, J., Wang, X., Zhang, L., Xu, C., Yang, G., & Li, X. (2022). Feature Re-Learning with Data Augmentation for Video Relevance Prediction. IEEE Transactions on Knowledge and Data Engineering, 34(3), 1184–1197. https://doi.org/10.1109/tkde.2019.2947442

Febrina Michelle, G., Modami, N., Eleazar, E., Manopo, R., Kurniawan, R., Enditama, D. R., & Ayunda, A. T. (2026). Information Security Evaluation Based on KAMI Index 5.0 (2023) at PT X. Jurnal Ilmiah Sistem Informasi, 5(2), 68–77. https://doi.org/10.51903/etg50932

Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023). Self-Supervised Learning for Medical Image Classification: A Systematic Review and Implementation Guidelines. Npj Digital Medicine, 6(1), 74. https://doi.org/10.1038/s41746-023-00811-0

Jiang, X., Zhu, R., Ji, P., & Li, S. (2023). Co-Embedding of Nodes and Edges with Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7075–7086. https://doi.org/10.1109/tpami.2020.3029762

Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., Yang, J., Yuan, J., Zhao, Y., Wang, Y., Luo, X., & Zhang, M. (2024). A Comprehensive Survey on Deep Graph Representation Learning. Neural Networks, 173, 106207. https://doi.org/10.1016/j.neunet.2024.106207

Kumar, P., Rawat, P., & Chauhan, S. (2022). Contrastive Self-Supervised Learning: Review, Progress, Challenges and Future Research Directions. International Journal of Multimedia Information Retrieval, 11(4), 461–488. https://doi.org/10.1007/s13735-022-00245-6

Li, J. (2025). Legal Information Extraction and Classification Using BERT, BI-LSTM, and CRF Models. Journal of Computational Methods in Sciences and Engineering, 25(4), 3509–3522. https://doi.org/10.1177/14727978251323131

Li, Z., Huang, C., Xia, L., Xu, Y., & Pei, J. (2022). Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2984–2996. https://doi.org/10.1109/icde53745.2022.00269

Luscombe, A., Duncan, J., & Walby, K. (2022). Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies. Journal of Criminal Justice Education, 33(2), 151–171. https://doi.org/10.1080/10511253.2022.2027477

Mai, N. T., & Khalid, I. (2025). Human Error vs. System Security: Evaluating the Weakest Link in Digital Business Information Systems. Journal of Management and Informatics, 4(3), 981–997. https://doi.org/10.51903/jmi.v4i3.305

Mantoro, T., Permana, M. A., & Anugerah Ayu, M. (2022). Crime Index Based on Text Mining on Social Media Using Multi Classifier Neural-Net Algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control), 20(3), 570. https://doi.org/10.12928/telkomnika.v20i3.23321

Matereke, T., Nyirenda, C. N., & Ghaziasgar, M. (2021). A Performance Evaluation of 3D Deep Learning Algorithms for Crime Classification. 2021 IEEE AFRICON, 1–6. https://doi.org/10.1109/africon51333.2021.9570983

Pandey, A., Fanuel, M., Schreurs, J., & Suykens, J. A. K. (2022). Disentangled Representation Learning and Generation with Manifold Optimization. Neural Computation, 34(10), 2009–2036. https://doi.org/10.1162/neco_a_01528

Park, Y., Park, R. S., & Kim, H. (2024). Key Information Extraction for Crime Investigation by Hybrid Classification Model. Electronics, 13(8), 1525. https://doi.org/10.3390/electronics13081525

Raharjo, B., Rudjiono, & Fitrianto, Y. (2024). Prediction and Detection of Scam Threats on Digital Platforms for Indonesian Users Using Machine Learning Models. Journal of Technology Informatics and Engineering, 3(3), 350–369. https://doi.org/10.51903/jtie.v3i3.208

Schiappa, M. C., Rawat, Y. S., & Shah, M. (2023). Self-Supervised Learning for Videos: A Survey. ACM Computing Surveys, 55(13), 1–37. https://doi.org/10.1145/3577925

Valois, P. H. V., Macedo, J., Ribeiro, L. S. F., dos Santos, J. A., & Avila, S. (2025). Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery. Forensic Science International: Digital Investigation, 53, 301918. https://doi.org/10.1016/j.fsidi.2025.301918

Ye, Z., Yao, L., Zhang, Y., & Gustin, S. (2024). Self-Supervised Cross-Modal Visual Retrieval From Brain Activities. Pattern Recognition, 145, 109915. https://doi.org/10.1016/j.patcog.2023.109915

Zheng, Y., Jin, M., Liu, Y., Chi, L., Phan, K. T., & Chen, Y.-P. P. (2023). Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12220–12233. https://doi.org/10.1109/tkde.2021.3119326

Zhu, Y., Shuai, H., Liu, G., & Liu, Q. (2022). Self-Supervised Video Representation Learning Using Improved Instance-Wise Contrastive Learning and Deep Clustering. IEEE Transactions on Circuits and Systems for Video Technology, 32(10), 6741–6752. https://doi.org/10.1109/tcsvt.2022.3169469

Zong, Y., Aodha, O. Mac, & Hospedales, T. M. (2025). Self-Supervised Multimodal Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(7), 5299–5318. https://doi.org/10.1109/tpami.2024.3429301