Role of Artificial Intelligence in the Prevention of Online Child Sexual Abuse: A Systematic Review of Literature

References

• Abajobir, A. A., Kisely, S., Maravilla, J. C., Williams, G., & Najman, J. M. (2017). Gender differences in the association between childhood sexual abuse and risky sexual behaviours: A systematic review and meta-analysis. Child Abuse & Neglect, 63, 249–260. https://doi.org/10.1016/j.chiabu.2016.11.023
   PubMed Web of Science ®Google Scholar
• Açar, K. V. (2017). Webcam child prostitution: An exploration of current and futuristic methods of detection. International Journal of Cyber Criminology, 11(1), 98–109.
   Google Scholar
• Agarwal, N., Ünlü, T., Wani, M. A., & Bours, P. (2021). Predatory conversation detection using transfer learning approach. In International Conference on Machine Learning, Optimization, and Data Science (pp. 488–499). Springer International Publishing.
   Google Scholar
• Anderson, P., Zuo, Z., Yang, L., & Qu, Y. (2019). An intelligent online grooming detection system using AI technologies [Paper Presentation]. 2019 IEEE International Conference on Fuzzy Systems (Fuzz-IEEE) (pp. 1–6). https://doi.org/10.1109/Fuzz-Ieee.2019.8858973
   Google Scholar
• Bogdanova, D., Rosso, P., & Solorio, T. (2014). Exploring high-level features for detecting cyberpedophilia. Computer Speech & Language, 28(1), 108–120. https://doi.org/10.1016/j.csl.2013.04.007
   Web of Science ®Google Scholar
• Bhunu Shava, F., Rita, M. N., & Chitauro, M. (2022). Tech4Good: Artificial intelligence powered chatbots with child online protection in mind. SSRN 4331466.
   Google Scholar
• Borj, P. R., Raja, K., & Bours, P. (2022). Online grooming detection: A comprehensive survey of child exploitation in chat logs. Knowledge-Based Systems, 259, 110039. https://doi.org/10.1016/j.knosys.2022.110039
   Web of Science ®Google Scholar
• Broadhurst, R. (2019). Child sex abuse images and exploitation materials. In R. Leukfeldt & T. Holt (Eds.), Cybercrime: The human factor. Routledge.
   Google Scholar
• Broadhurst, R., & Ball, M. (2021). Tor’s underworld, ‘onion services’ and child sex abuse material: Submission to the Australian Parliamentary Joint Committee on Law Enforcement inquiry into ‘Law enforcement capabilities in relation to child exploitation’. SSRN 3927628.
   Google Scholar
• Christensen, L. S., Rayment-McHugh, S., Edwards, G., & Jones, C. (2021). Cyber strategies used to combat child sexual abuse material. Trends and Issues in Crime and Criminal Justice [Electronic Resource], 636, 1–16.
   Google Scholar
• Cronin, C., Sood, S., & Thomas, D. (2017). From innovation to transcreation: Adapting digital technologies to address violence against children. Child Abuse Review, 26(3), 215–229. https://doi.org/10.1002/car.2447
   Web of Science ®Google Scholar
• Devasahayam, T. W. (2019). Sex, crime and deceit: Women and child trafficking and sexual abuse in the internet age in Cambodia and Thailand. In Exploring the nexus between technologies and human rights (Vol. 1). SHAPE-SEA.
   Google Scholar
• Eggestein, J. V., & Knapp, K. J. (2014). Fighting child pornography: A review of legal and technological developments. Journal of Digital Forensics, Security and Law, 9(4), 3. https://doi.org/10.15394/jdfsl.2014.1191
   Google Scholar
• Europol. (2022). Our #TraceAnObject initiative was launched to help victim identification specialists solve cold cases of child sexual abuse. https://twitter.com/Europol/status/1535279727048642564
   Google Scholar
• Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018). Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: Randomized controlled trial. JMIR Mental Health, 5(4), e9782. https://doi.org/10.2196/mental.9782
   Web of Science ®Google Scholar
• Gangwar, A., González-Castro, V., Alegre, E., & Fidalgo, E. (2021). AttM-CNN: Attention and metric learning-based CNN for pornography, age and Child Sexual Abuse (CSA) Detection in images. Neurocomputing, 445, 81–104. https://doi.org/10.1016/j.neucom.2021.02.056
   Web of Science ®Google Scholar
• Gewirtz-Meydan, A., & Lahav, Y. (2020). Sexual functioning among childhood sexual abuse survivors from an attachment perspective. The Journal of Sexual Medicine, 17(7), 1370–1382. https://doi.org/10.1016/j.jsxm.2020.03.014
   PubMed Web of Science ®Google Scholar
• Gillingham, P. (2015). Predictive risk modelling to prevent child maltreatment and other adverse outcomes for service users: Inside the ‘black box’ of machine learning. British Journal of Social Work, 46(4), 1044–1058. https://doi.org/10.1093/bjsw/bcv031
   PubMed Web of Science ®Google Scholar
• Guerra, E., & Westlake, B. G. (2021). Detecting child sexual abuse images: Traits of child sexual exploitation hosting and displaying websites. Child Abuse & Neglect, 122, 105336. https://doi.org/10.1016/j.chiabu.2021.105336
   PubMed Web of Science ®Google Scholar
• Gurriell, M. (2021). Born into porn but rescued by thorn: The demand for tech companies to scan and search for child sexual abuse images. Family Court Review, 59(4), 840–854. https://doi.org/10.1111/fcre.12613
   Web of Science ®Google Scholar
• Haddaway, N. R., Macura, B., Whaley, P., & Pullin, A. S. (2018). ROSES Reporting standards for Systematic Evidence Syntheses: Pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environmental Evidence, 7(1), 8. https://doi.org/10.1186/s13750-018-0121-7
   Web of Science ®Google Scholar
• Hailes, H. P., Yu, R., Danese, A., & Fazel, S. (2019). Long-term outcomes of childhood sexual abuse: An umbrella review. The Lancet. Psychiatry, 6(10), 830–839. https://doi.org/10.1016/S2215-0366(19)30286-X
   PubMed Web of Science ®Google Scholar
• Hamidouche, W., Biatek, T., Abdoli, M., Francois, E., Pescador, F., Radosavljevic, M., Menard, D., & Raulet, M. (2022). Versatile video coding standard: A review from coding tools to consumers deployment. IEEE Consumer Electronics Magazine, 11(5), 10–24. https://doi.org/10.1109/MCE.2022.3144545
   Web of Science ®Google Scholar
• Horikawa, H., Suguimoto, S. P., Musumari, P. M., Techasrivichien, T., Ono-Kihara, M., & Kihara, M. (2016). Development of a prediction model for child maltreatment recurrence in Japan: A historical cohort study using data from a child guidance center. Child Abuse & Neglect, 67, 429–465. https://doi.org/10.1016/j.chiabu.2017.04.001
   Web of Science ®Google Scholar
• Ilbiz, E., & Kaunert, C. (2023). Crowdsourcing to tackle online child sexual exploitation: Europol’s ‘stop child abuse—trace an object’ platform. Policing: A Journal of Policy and Practice, 17, paad009. https://doi.org/10.1093/police/paad009
   Web of Science ®Google Scholar
• Internet Watch Foundation. (2023). Sexual abuse imagery of primary school children 1,000 per cent worse since lockdown. Internet Watch Foundation.
   Google Scholar
• Kakatkar, C., Bilgram, V., & Füller, J. (2020). Innovation analytics: Leveraging artificial intelligence in the innovation process. Business Horizons, 63(2), 171–181. https://doi.org/10.1016/j.bushor.2019.10.006
   Web of Science ®Google Scholar
• Komathy, K. (2022). Machine learning to detect the toxicity of web textual contents against gender-based cyberoffenses. In: A. Joshi, M. Mahmud, R. G. Ragel, & N. V. Thakur (Eds.), Information and communication technology for competitive strategies (ICTCS 2020). Lecture notes in networks and systems, Vol. 191.(pp. 877–885). Springer.
   Google Scholar
• Küepeli, C. (2019). Legal analysis of sextortion crime in the comparative law and Turkish Law. Health Sciences Quarterly, 3(5), 87–98.
   Google Scholar
• Lee, H. E., Ermakova, T., Ververis, V., & Fabian, B. (2020). Detecting child sexual abuse material: A comprehensive survey. Forensic Science International: Digital Investigation, 34, 301022.
   Web of Science ®Google Scholar
• Mathur, A., Munshi, H., Varma, S., Arora, A., & Singh, A. (2022). Effectiveness of artificial intelligence in cognitive behavioral therapy. In ICT with intelligent applications: Proceedings of ICTIS 2021 (Vol. 1, pp. 413–423). Springer Singapore.
   Google Scholar
• McGarvie, J. (2023). From hashtag to hash value: Using the hash value model to report child sex abuse material. Seattle Journal of Technology, Environmental & Innovation Law, 13(2), 4.
   Google Scholar
• Melander, G., Alfredsson, G., & Holmström, L. (2004). Optional protocol to the convention on the rights of the child on the sale of children, child prostitution and child pornography: Adopted by the general assembly of the United Nations on 25 May 2000. In The Raoul Wallenberg Institute compilation of human rights instruments (pp. 293–302). Brill Nijhoff.
   Google Scholar
• Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R., & Cheraghi-Sohi, S. (2014). PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Research, 14(1), 579. https://doi.org/10.1186/s12913-014-0579-0
   PubMed Web of Science ®Google Scholar
• Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
   PubMed Web of Science ®Google Scholar
• Mooney, J. L., & Ost, S. (2013). Group localised grooming: What is it and what challenges does it pose for society and law. Child and Family Law Quarterly, 25, 425.
   Google Scholar
• Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389–404. https://doi.org/10.1016/j.jbusres.2020.10.044
   Web of Science ®Google Scholar
• Ngo, V. M., Thorpe, C., Dang, C. N., & Mckeever, S. (2022). Investigation, detection and prevention of online child sexual abuse materials: A comprehensive survey. In 2022 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 707–713). IEEE.
   Google Scholar
• Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37, 879–910. https://doi.org/10.17705/1CAIS.03743
   Web of Science ®Google Scholar
• Online child sexual exploitation and abuse. (2022). Module 12: Interpersonal cybercrime. United Nations Office on Drugs and Crime.
   Google Scholar
• Povedano Álvarez, D., Sandoval Orozco, A. L., García-Miguel, J. P., & García Villalba, L. J. (2023). Learning strategies for sensitive content detection. Electronics, 12(11), 2496. https://doi.org/10.3390/electronics12112496
   Web of Science ®Google Scholar
• Pulverman, C. S., Kilimnik, C. D., & Meston, C. M. (2018). The impact of childhood sexual abuse on women’s sexual health: A comprehensive review. Sexual Medicine Reviews, 6(2), 188–200. https://doi.org/10.1016/j.sxmr.2017.12.002
   PubMed Web of Science ®Google Scholar
• Ramírez Sánchez, J., Campo-Archbold, A., Zapata Rozo, A., Díaz-López, D., Pastor-Galindo, J., Gómez Mármol, F., & Aponte Díaz, J. (2021). Uncovering cybercrimes in social media through natural language processing. Complexity, 2021, 1–15. https://doi.org/10.1155/2021/7955637
   Google Scholar
• Rodríguez, J. I., Durán, S. R., Díaz-López, D., Pastor-Galindo, J., & Mármol, F. G. (2020). C 3-Sex: A conversational agent to detect online sex offenders. Electronics, 9(11), 1779. https://doi.org/10.3390/electronics9111779
   Web of Science ®Google Scholar
• Scanlan, J., Watters, P. A., Prichard, J., Hunn, C., Spiranovic, C., & Wortley, R. (2022). Creating honeypots to prevent online child exploitation. Future Internet, 14(4), 121. https://doi.org/10.3390/fi14040121
   Web of Science ®Google Scholar
• Seigfried-Spellar, K. C., Rogers, M. K., Rayz, J. T., Yang, S. F., Misra, K., & Ringenberg, T. (2019). Chat Analysis Triage Tool: Differentiating contact-driven vs. fantasy-driven child sex offenders. Forensic Science International, 297, e8–e10. https://doi.org/10.1016/j.forsciint.2019.02.028
   PubMed Web of Science ®Google Scholar
• Shaffril, H. A. M., Samah, A. A., & Samsuddin, S. F. (2021). Guidelines for developing a systematic literature review for studies related to climate change adaptation. Environmental Science and Pollution Research International, 28(18), 22265–22277. https://doi.org/10.1007/s11356-021-13178-0
   PubMed Web of Science ®Google Scholar
• Shekhar, B., & Vincent, V. V. (2022). Technology and human trafficking: Reconstructing the nexus. Urban Crime. An International Journal, 3(1), 65–101.
   Google Scholar
• Shenk, C. E., Felt, J. M., Ram, N., O'Donnell, K. J., Sliwinski, M. J., Pokhvisneva, I., Benson, L., Meaney, M. J., Putnam, F. W., & Noll, J. G. (2022). Cortisol trajectories measured prospectively across thirty years of female development following exposure to childhood sexual abuse: moderation by epigenetic age acceleration at midlife. Psychoneuroendocrinology, 136, 105606. https://doi.org/10.1016/j.psyneuen.2021.105606
   PubMed Web of Science ®Google Scholar
• Socatiyanurak, V., Klangpornkun, N., Munthuli, A., Phienphanich, P., Kovudhikulrungsri, L., Saksakulkunakorn, N., Chairaungsri, P., & Tantibundhit, C. (2021). Law-u: Legal guidance through artificial intelligence chatbot for sexual violence victims and survivors. IEEE Access. 9, 131440–131461. https://doi.org/10.1109/ACCESS.2021.3113172
   Google Scholar
• Stănilă, L. (2020). Memories of the future-sweetie and the impact of the new technologies on the criminal justice system. EU and Comparative Law Issues and Challenges Series (ECLIC), 4, 557–575.
   Google Scholar
• Tabi, C, Hewage, C, Bakhsh, S.T., & Ukwandu, E. (2023). Contemporary issues in child protection: Police use of artificial intelligence for online child protection in the UK In: R. Montasari, V. Carpenter, A. J. Masys, (Eds.), Digital transformation in policing: The promise, perils and solutions. Advanced sciences and technologies for security applications. (pp. 85–107). Springer.
   Google Scholar
• Urbas, G. (2021). Legal considerations in the use of artificial intelligence in the investigation of online child exploitation. ANU College of Law Research Paper (21.44).
   Google Scholar
• Van der Watt, M. (2023). Discouraging the demand that fosters sex trafficking: Collaboration through augmented intelligence. Societies, 13(4), 94. https://doi.org/10.3390/soc13040094
   Web of Science ®Google Scholar
• Vaillancourt-Morel, M. P., Godbout, N., Labadie, C., Runtz, M., Lussier, Y., & Sabourin, S. (2015). Avoidant and compulsive sexual behaviors in male and female survivors of childhood sexual abuse. Child Abuse & Neglect, 40, 48–59. https://doi.org/10.1016/j.chiabu.2014.10.024
   PubMed Web of Science ®Google Scholar
• Vásquez-Correa, J. C., & Álvarez Muniain, A. (2023). Novel speech recognition systems applied to forensics within child exploitation: Wav2vec2. 0 vs. whisper. Sensors, 23(4), 1843. https://doi.org/10.3390/s23041843
   PubMed Web of Science ®Google Scholar
• Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15(3), 398–405. https://doi.org/10.1111/nhs.12048
   PubMed Web of Science ®Google Scholar
• Vosz, M., McPherson, L., Tucci, J., Mitchell, J., Fernandes, C., & Macnamara, N. (2023). It is time to focus on prevention: A scoping review of practices associated with prevention of child sexual abuse and australian policy implications. International Journal on Child Maltreatment: Research, Policy and Practice, 6(1), 79–107. https://doi.org/10.1007/s42448-022-00143-8
   Google Scholar
• Wani, M. A., Agarwal, N., & Bours, P. (2021). Sexual-predator detection system based on social behavior biometric (SSB) features. Procedia Computer Science, 189, 116–127. https://doi.org/10.1016/j.procs.2021.05.075
   Google Scholar
• Wakida, E. K., Talib, Z. M., Akena, D., Okello, E. S., Kinengyere, A., Mindra, A., & Obua, C. (2018). Barriers and facilitators to the integration of mental health services into primary health care: A systematic review. Systematic Reviews, 7(1), 211. https://doi.org/10.1186/s13643-018-0882-7
   PubMed Web of Science ®Google Scholar
• Westlake, B. G. (2020). The past, present, and future of online child sexual exploitation: Summarizing the evolution of production, distribution, and detection. In The Palgrave handbook of international cybercrime and cyberdeviance (pp. 1225–1253). Palgrave.
   Google Scholar
• Winters, G. M., & Jeglic, E. L. (2017). Stages of sexual grooming: Recognizing potentially predatory behaviors of child molesters. Deviant Behavior, 38(6), 724–733. https://doi.org/10.1080/01639625.2016.1197656
   Web of Science ®Google Scholar
• World Health Organization. (2017). Responding to children and adolescents who have been sexually abused. WHO clinical guidelines.
   Google Scholar
• World Health Organization. (2022). Child maltreatment. https://www.who.int/news-room/fact-sheets/detail/child-maltreatment.
   Google Scholar
• Zambrano, P., Torres, J., Tello-Oquendo, L., Jácome, R., Benalcazar, M. E., Andrade, R., & Fuertes, W. (2019). Technical mapping of the grooming anatomy using machine learning paradigms: An information security approach. IEEE Access, 7, 142129–142146. https://doi.org/10.1109/ACCESS.2019.2942805
   Google Scholar