Four approaches, different kinds of evidence
Interpretation. The research covers learning activities, letters to suspected fraud victims, automated detection, and support after a scam. These approaches serve different purposes. Their results should be read against what researchers actually measured, rather than treated as interchangeable evidence of protection (Cross, 2016; Gould et al., 2021; Lea et al., 2024; Ranaweera & Neiat, 2026).
| Focus | What the studies show | What remains uncertain |
|---|---|---|
| Interactive education Small virtual reality usability pilot | Ten participants gave HeartGuard VR usability scores above the study’s cited average benchmark (Lea et al., 2024). | Interpretation. The pilot assessed usability, not lasting protection from scams. Its sample was small, and whether participants retained what they learned remained unresolved (Lea et al., 2024). |
| Payment warnings Australian program monitoring | Many Project Sunbird recipients stopped or reduced monitored money transfers after receiving a first warning letter (Cross, 2016). | The data could not establish whether receiving the letter caused transfers to stop (Cross, 2016). |
| Conversation detection Synthetic dialogue dataset and proposed uses | A dataset of synthetic dialogue—artificially generated conversations—was intended to train systems to distinguish fraudulent conversations from legitimate ones (Ranaweera & Neiat, 2026). | The authors warn that systems trained only on synthetic data may miss real-world scam variants (Ranaweera & Neiat, 2026). |
| Support after brain injury Qualitative studies of experiences and service needs | Participants wanted simple, repeated scam education and better information and support for people close to survivors (Gould et al., 2021). | Evidence on how clinicians and service providers help scam survivors with acquired brain injury remains limited (Chew et al., 2024). |
A money warning needs to explain the relationship
In an analysis of 1,015 romance-fraud cases, 558 (55%) included details about what led someone to recognize the scam. These included further money requests, features of messages, checking information, offenders’ actions, and intervention by another person (Cross, 2023).
Interpretation. The study noted that recognition came after financial loss and called for earlier awareness of these factors. That distinction matters: identifying what eventually raised suspicion does not show that presenting the same information beforehand would have prevented a transfer (Cross, 2023).
Interpretation. A request for money or personal information is a concrete focus for a warning. Yet a rule such as “don’t send money” leaves out the bond and trust the offender has developed. Research warns that this simplification can obscure grooming and abuse and encourage victims to blame themselves (Cross, 2022; Kassem & Carter, 2023).
A usable lesson is not yet a proven safeguard
HeartGuard VR is a virtual reality game designed to teach the public about romance scams. Its August 2023 evaluation recruited 10 participants through advertising at Abertay University and on social media. The experience took approximately one hour. The evaluation used questionnaires about usability and participants' experiences (Lea et al., 2024).
Participants rated the game's usability at a mean of 79.75, above the study's benchmark of 68 for average usability. However, answers to questions intended to measure the same thing did not form a consistent pattern in this small group. Participants were generally positive, but the education questions also showed weak consistency, limiting confidence in those ratings as a dependable measure of educational value (Lea et al., 2024).
Interpretation. Some participants wanted a branching story and meaningful consequences for their choices. The authors also called for follow-up to determine whether people retained what they learned. These suggestions concern two different tasks: improving the experience and checking what remains useful after it ends (Lea et al., 2024).
Payments changed after letters—but why?
A 2016 study examined three Australian programs addressing suspected online fraud, not romance scams alone. In Project Sunbird, agencies reviewed records of transfers to five countries and excluded transactions judged likely legitimate. They then sent letters to the remaining households explaining the suspected fraud and encouraging recipients to stop transferring money (Cross, 2016).
Among senders who received a first letter between March 2013 and July 2015, 73% stopped transfers covered by the program's monitoring and 13% reduced the amount sent within that coverage. These figures do not necessarily describe all payments. The study explicitly stated that its data could not directly link receipt of a letter to stopped transactions (Cross, 2016).
The other programs also illustrate why monitoring details matter. The National Scams Disruption Project reported that 77% of first-letter recipients had no overseas transfers recorded within its monitoring coverage during at least six weeks of follow-up. Across jurisdictions, however, monitoring covered different countries and periods, so the percentages cannot establish which program worked better (Cross, 2016).
Detection must work beyond prepared examples
Automated detection can look for features of suspicious communications. Earlier work proposed identifying reused romance-scam text by comparing incoming messages with a collection of known scripts. This proposal explains how messages might be flagged; it does not by itself establish that using the method reduces victimization (Jakobsson, 2016).
A newer dataset is intended to train software to distinguish fraudulent conversations from legitimate ones or intervene as scams unfold. Relying only on its generated conversations could leave systems unprepared for real-world scam variants. The authors therefore recommend supplementing training for real-world use with ethically obtained real conversations, rather than assuming performance on generated examples will carry over (Ranaweera & Neiat, 2026).
Interpretation. Another detection paper identifies false positives—legitimate activity wrongly flagged as suspicious—and difficulty adapting to changing tactics as problems with existing methods. A service considering automation therefore needs evidence about both missed scams and mistaken warnings, not merely confirmation that an alerting feature exists (Vedhanayagam et al., 2026).
Support must fit the person receiving it
Research involving survivors with acquired brain injury describes needs that standard awareness material may not meet. Participants suggested simple training repeated across sessions, including mock scams, games, and workshops. They also wanted safe-online-dating education that preserved the benefits of dating rather than treating avoidance as the only goal (Gould et al., 2021).
Participants viewed hearing from another scam survivor as helpful for reducing isolation, disbelief, and blame and encouraging help-seeking. People close to survivors wanted a central information resource and support groups (Gould et al., 2021).
A 2024 study of clinicians and service providers found little evidence about how the workforce responds to scams involving acquired brain injury. The authors called for additional evidence on prevention and treatment. That identifies uncertainty about effective support; it does not show that nobody offers care (Chew et al., 2024).
What program planners can change
Interpretation. Use the following suggestions when designing a service and deciding whether to keep it. Build evaluation into the plan while the service can still be revised (Cross, 2022).
Pair the request with the manipulation
Review the examples in warning material. Do they show how an apparent relationship makes a request for money or personal information persuasive? Explain the grooming and pressure around the request, and remove wording that blames people for failing to follow a simple rule (Cross, 2022; Kassem & Carter, 2023).
Measure the benefit being promised
Match measures to the promise: test learning again later, and distinguish losses to fraud from changes in monitored transfers. Check whether payments moved to another channel or destination. Comparing recipients with people who did not receive a warning would help assess what the warning itself contributed (Cross, 2016; Lea et al., 2024).
Build access and error checks into the plan
Ask whether education can accommodate cognitive impairments and whether people close to survivors can find relevant information. For automated tools, require testing on ethically obtained real-world data, track mistaken flags and missed scams, and include human review of flagged activity. Treat these as operational safeguards to evaluate, not guarantees of reliable protection (Gould et al., 2021; Ranaweera & Neiat, 2026; Vedhanayagam et al., 2026).
Before expanding a pilot
Before expanding a pilot, decide what finding would make you stop or revise it. In a payment-warning program, fewer transfers could include legitimate payments as well as fraud losses. What evidence would let you distinguish those outcomes? (Cross, 2016, 2022).
About the evidence
This explanation draws on selected sources from RSRC’s full synthesis. The full research record, its source list and version history remain available below.
Scope, preparation and limitations
AI prepared this explanation from synthesis version 99, then performed separate editorial grading and source-support checks. The automated grades assess communication; they are not reader-test results or measures of scientific certainty. No human or expert content review of this explanation is recorded.
The full synthesis preserves 21 publication records and 80 evidence statements. These counts do not establish the number of independent studies or the strength of a finding. Practical implications are editorial interpretations, with limitations explained alongside the evidence.
RSRC methodologySources cited in this explanation (10)
Links open the publication record or original work. Journal access may vary.
- Chew, K. A., Ponsford, J., & Gould, K. R. (2024). Addressing Cyberscams and Acquired Brain Injury (“I Desperately Need to Know What to Do”): Qualitative Exploration of Clinicians’ and Service Providers’ Perspectives. Journal of Medical Internet Research, 26, e51245. https://doi.org/10.2196/51245
- Cross, C. (2016). Using financial intelligence to target online fraud victimisation: applying a tertiary prevention perspective. Criminal Justice Studies, 29(2), 125–142. https://doi.org/10.1080/1478601x.2016.1170278
- Cross, C. (2022). Meeting the Challenges of Fraud in a Digital World. The Handbook of Security, 217–238. https://doi.org/10.1007/978-3-030-91735-7_11
- Cross, C. (2023). “I knew it was a scam”: Understanding the triggers for recognizing romance fraud. Criminology & Public Policy, 22(4), 613–637. https://doi.org/10.1111/1745-9133.12645
- Gould, K. R., Carminati, J.-Y. J., & Ponsford, J. L. (2021). “They just say how stupid I was for being conned”. Cyberscams and acquired brain injury: A qualitative exploration of the lived experience of survivors and close others. Neuropsychological Rehabilitation, 33(2), 325–345. https://doi.org/10.1080/09602011.2021.2016447
- Jakobsson, M. (2016). Understanding Social Engineering Based Scams. https://doi.org/10.1007/978-1-4939-6457-4
- Kassem, R., & Carter, E. (2023). Mapping romance fraud research – a systematic review. Journal of Financial Crime, 31(4), 974–992. https://doi.org/10.1108/jfc-06-2023-0160
- Lea, O., Shepherd, L. A., & Szymkowiak, A. (2024). HeartGuard VR: Immersive Romance Scam Education. Lecture notes in computer science, 201–211. https://doi.org/10.1007/978-3-031-78269-5_19
- Ranaweera, K., & Neiat, A. G. (2026). Synthetic Dialogue Dataset for Romance Scam Detection. Harvard Dataverse. https://doi.org/10.7910/dvn/o317px
- Vedhanayagam, P., Singh, M., Dadhania, A. P., & Ikram, S. T. (2026). Forensic footprints in digital love: Unveiling romance scams with Maltego and machine learning. AIP conference proceedings, 3449, 020278. https://doi.org/10.1063/5.0298575
Full synthesis and revision history
Download the full synthesis PDF
Reviewed evidence supports a multi-layered prevention and intervention approach that aligns timing and modality to commonly observed staged grooming-to-extraction sequences. Multiple qualitative syntheses and conversation-based studies converge on a staged model in which offenders build interpersonal rapport and trust before escalating to monetary requests or investment solicitations; this staging creates distinct upstream (recruitment/rapport) and downstream (monetary-extraction) windows for different prevention tactics. Large analyses of victim reports identify clustered recognition triggers—additional money requests, communication anomalies, failed verification, offender actions, and third‑party involvement—and show that many triggers are only noticed or acted upon after financial loss, which implies prevention should aim to shift recognition earlier in relationship timelines. Prototype interactive education (including a small VR prototype) and contextualized simulated training show promising usability and feasibility signals but remain unproven for reducing real-world victimization because evaluations are small, nonlongitudinal, or method-limited. Administrative financial‑intelligence interventions that screened transfers and issued warning letters reported notable short‑window behavioral signals—for example, program statistics showing large proportions of first‑letter recipients stopped or reduced transfers during monitoring—but available program descriptions and administrative data cannot establish causation and monitoring windows varied across programs. Automated detection and OSINT workflows (including blockchain tracing and ML proposals) are promising for scaling detection and enabling alerts, but reviewed detection work identifies high false‑positive rates, adaptation challenges, and limited external validation; therefore, production deployment should be coupled with ethically obtained real‑world validation data and human‑in‑the‑loop moderation. Specialized prevention and recovery needs for older adults and people with acquired brain injury (ABI) are consistently identified, and co‑design and clinician‑perspective work propose tailored, simple, repeated training and central support resources; nevertheless, the reviewed corpus contains no validated clinical treatment guidelines or outcome‑evaluated interventions for these groups (Chew et al., 2024; Cross, 2016, 2023; Gould et al., 2021; Jakobsson, 2016; Lea et al., 2024; Ranaweera & Neiat, 2026; Vedhanayagam et al., 2026; Whitty, 2013a, 2013b).
Staged progression and stage-aware interventions
Multiple qualitative syntheses and conversation-based studies converge on a staged model in which offenders build interpersonal rapport and trust before escalating to monetary requests or investment solicitations; this staging creates distinct upstream (recruitment/rapport) and downstream (monetary-extraction) windows for different prevention tactics (Wang & Zhou, 2022; Whitty, 2013a, 2013b).
This stage-based description appears across multiple contexts and supports designing stage‑aware interventions: upstream measures focus on recruitment and verification barriers, while downstream measures address transaction monitoring, warnings, and recovery support once financial requests begin. The staging implication motivates separating prevention activities by their likely point of influence in the relationship timeline rather than treating all advice as equivalent (Wang & Zhou, 2022; Whitty, 2013a, 2013b).
Recognition triggers and messaging
Large analyses of victim reports identify clustered recognition triggers—additional money requests, communication anomalies, failed verification, offender actions, and third‑party involvement—and show that many triggers are only noticed or acted upon after financial loss, which implies prevention should aim to shift recognition earlier in relationship timelines (Cross, 2023).
Because many victims recognise red flags late, reviewed authors recommend broadening prevention messaging beyond single admonitions. Several contributions explicitly advise developing educational content that links specific, proximate indicators (for example, unexpected money requests or refusal to provide verifiable identity checks) to concrete actions a potential victim can take before transferring funds (Cross, 2023; Kassem & Carter, 2023; Whitty, 2013b).
Education formats, prototypes, and evidence limits
Prototype interactive tools have been piloted: an immersive VR serious‑game (HeartGuard VR) produced above‑average usability scores and positive experiential feedback in a small evaluation, but the sample (n=10), limited internal-consistency metrics, and absence of longitudinal behavioral outcome data mean effectiveness for reducing real-world victimization remains unproven (Lea et al., 2024).
Other contextualized awareness prototypes and mock platform exercises have been developed to increase engagement beyond static text; several publications describe prototype designs, brief pre/post questionnaires, or note that existing official information is often non‑interactive. These method reports and gap statements lead authors to call for larger, controlled trials and rigorous evaluation frameworks to establish whether interactive or contextualized formats produce durable behavior change (Cross, 2022; Dickerson et al., 2020; Lea et al., 2024).
Automated detection, OSINT, and operational cautions
Automated detection and OSINT workflows (including blockchain tracing and ML proposals) are promising for scaling detection and enabling alerts, but reviewed detection work either lacks external validation on representative conversational datasets or is explicitly limited by adaptation challenges and likely high false‑positive rates; therefore, production deployment should be coupled with ethically obtained real‑world validation data and human‑in‑the‑loop moderation (Jakobsson, 2016; Ranaweera & Neiat, 2026; Vedhanayagam et al., 2026).
Reviewed detection work identifies methodological limitations that matter for production use: authors warn of high false‑positive rates and weak adaptation to changing scam tactics, and synthetic‑data benchmarks are explicitly acknowledged as limited because models trained exclusively on artificial dialogues may not generalize to real-world variants. Consequently, several contributions recommend supplementing classifiers with ethically obtained real-world data and conducting external validation before deploying production systems (Jakobsson, 2016; Ranaweera & Neiat, 2026; Vedhanayagam et al., 2026).
A subset of reviewed detection-method work documents prototype system behavior and future development plans: one implemented system autonomously alerts users about suspicious activities and maintains a blacklist of identified malicious or dubious sites, and other authors propose automated real-time alerting as a future development direction (Vedhanayagam et al., 2026).
Platform, financial‑sector, and administrative interventions
Administrative financial‑intelligence programs that screened transfers and issued warning letters reported notable short‑window behavioral signals—for example, program statistics showing large proportions of first‑letter recipients stopped or reduced transfers during monitoring—but available program descriptions and administrative data cannot establish causation because of absent controls, heterogeneous monitoring windows, and jurisdictional variation (Cross, 2016).
Platform- and bank‑oriented proposals across the reviewed corpus recommend measures such as periodic re‑verification at account creation, multiple profile images, suspicious-account monitoring, account whitelists, and transfer-warning pop‑ups; these technical and partnership proposals are primarily future‑oriented recommendations rather than externally validated effectiveness findings (Tan, 2023; Vedhanayagam et al., 2026).
Special-population needs and service responses
Older adults and people with acquired brain injury (ABI) are repeatedly identified as groups requiring tailored prevention and recovery approaches: the ABI literature calls for simple, repeated one‑to‑one training, caregiver education, central support resources, and co‑designed psychosocial recovery frameworks, while elder‑focused work recommends social‑connection interventions, directed teaching, and technical‑guardian supports; nevertheless, the reviewed corpus contains no validated clinical treatment guidelines or outcome‑evaluated interventions for these groups (Chew et al., 2024; Gould et al., 2021; Ma & McKinnon, 2021).
Clinician and service‑provider studies emphasise prompt, informed referral pathways and improved workforce awareness of ABI-related accommodations. Several authors call for pilot evaluations of tailored prevention and recovery programs (for example, caregiver-centred supports, central resource hubs, and evaluated training protocols) to establish evidence of effectiveness for these vulnerable groups (Chew et al., 2024; Gould et al., 2021).
Community coordination, awareness campaigns, and evidence limits
Multiple studies and legal‑policy analyses recommend cross‑sector coordination—platforms, banks, law enforcement, victim services, and community organizations—to scale prevention and support; suggested measures include coordinated monitoring, targeted awareness campaigns, employee training in financial services, and exchange‑level detection or inducement‑testing to enable timely warnings and account action (Cross, 2016; Griffin & Mei, 2024; Hasibuan & Syam, 2023; L. Burton & D. (Vickerson) Moore, 2024).
Community‑level awareness programs and localized outreach (including geographically or demographically tailored campaigns) are commonly recommended, but many documented community interventions report descriptive implementation and short‑term acceptability without validated outcome measures or follow‑up, limiting conclusions about effectiveness (Annadorai et al., 2020; Pramana et al., 2024).
Detection-research limitations recur across the corpus: several authors note high false‑positive rates, weak adaptation to new scam tactics, and reliance on synthetic or limited labelled data; they recommend creating ethically sourced real conversational datasets, external validation protocols, and human‑in‑the‑loop evaluation before deploying automated classifiers at scale (Ranaweera & Neiat, 2026; Vedhanayagam et al., 2026).
References
- Annadorai, K., Krish, P., Shaari, A. H., & Kamaluddin, M. R. (2020). Mapping Computer Mediated Communication Theories and Persuasive Strategies in Analysing Online Dating Romance Scam. Journal of Xidian University, 14(5). https://doi.org/10.37896/jxu14.5/070 Publication record
- Chew, K. A., Ponsford, J., & Gould, K. R. (2024). Addressing Cyberscams and Acquired Brain Injury (“I Desperately Need to Know What to Do”): Qualitative Exploration of Clinicians’ and Service Providers’ Perspectives. Journal of Medical Internet Research, 26, e51245. https://doi.org/10.2196/51245 Publication record
- Cross, C. (2016). Using financial intelligence to target online fraud victimisation: applying a tertiary prevention perspective. Criminal Justice Studies, 29(2), 125–142. https://doi.org/10.1080/1478601x.2016.1170278 Publication record
- Cross, C. (2022). Meeting the Challenges of Fraud in a Digital World. The Handbook of Security, 217–238. https://doi.org/10.1007/978-3-030-91735-7_11 Publication record
- Cross, C. (2023). “I knew it was a scam”: Understanding the triggers for recognizing romance fraud. Criminology & Public Policy, 22(4), 613–637. https://doi.org/10.1111/1745-9133.12645 Publication record
- Dickerson, S., Apeh, E., & Ollis, G. (2020). Contextualised Cyber Security Awareness Approach for Online Romance Fraud. 2020 7th International Conference on Behavioural and Social Computing (BESC), 1–6. https://doi.org/10.1109/besc51023.2020.9348307 Publication record
- Gould, K. R., Carminati, J.-Y. J., & Ponsford, J. L. (2021). “They just say how stupid I was for being conned”. Cyberscams and acquired brain injury: A qualitative exploration of the lived experience of survivors and close others. Neuropsychological Rehabilitation, 33(2), 325–345. https://doi.org/10.1080/09602011.2021.2016447 Publication record
- Griffin, J. M., & Mei, K. (2024). How Do Crypto Flows Finance Slavery? The Economics of Pig Butchering. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4742235 Publication record
- Hasibuan, J., & Syam, S. (2023). A Legal Analysis on Online Fraud Using Fake Identity. Indonesian Journal of Multidisciplinary Science, 2(10), 3308–3317. https://doi.org/10.55324/ijoms.v2i10.574 Publication record
- Jakobsson, M. (2016). Understanding Social Engineering Based Scams. https://doi.org/10.1007/978-1-4939-6457-4 Publication record
- Kassem, R., & Carter, E. (2023). Mapping romance fraud research – a systematic review. Journal of Financial Crime, 31(4), 974–992. https://doi.org/10.1108/jfc-06-2023-0160 Publication record
- L. Burton, D. S., & D. (Vickerson) Moore, D. P. (2024). Pig Butchering in Cybersecurity: A Modern Social Engineering Threat. SocioEconomic Challenges, 8(3), 46–60. https://doi.org/10.61093/sec.8(3).46-60.2024 Publication record
- Lea, O., Shepherd, L. A., & Szymkowiak, A. (2024). HeartGuard VR: Immersive Romance Scam Education. Lecture notes in computer science, 201–211. https://doi.org/10.1007/978-3-031-78269-5_19 Publication record
- Ma, K. W. F., & McKinnon, T. (2021). COVID-19 and cyber fraud: emerging threats during the pandemic. Journal of Financial Crime, 29(2), 433–446. https://doi.org/10.1108/jfc-01-2021-0016 Publication record
- Pramana, P., Priastuty, C. W., & Utari, P. (2024). Waspada Cinta Maya: Membangun Kesadaran Bahaya Online Love Scam Masyarakat Desa 'Damai' Nglinggi, Kabupaten Klaten. Dharma Sevanam : Jurnal Pengabdian Masyarakat, 3(1), 1–9. https://doi.org/10.53977/sjpkm.v3i1.1205 Publication record
- Ranaweera, K., & Neiat, A. G. (2026). Synthetic Dialogue Dataset for Romance Scam Detection. Harvard Dataverse. https://doi.org/10.7910/dvn/o317px Publication record
- Tan, X. (2023). The New Network Fraud of “Pig Butchering” from the Perspective of Criminal Law. Lecture Notes in Education Psychology and Public Media, 17(1), 57–62. https://doi.org/10.54254/2753-7048/17/20231215 Publication record
- Vedhanayagam, P., Singh, M., Dadhania, A. P., & Ikram, S. T. (2026). Forensic footprints in digital love: Unveiling romance scams with Maltego and machine learning. AIP conference proceedings, 3449, 020278. https://doi.org/10.1063/5.0298575 Publication record
- Wang, F., & Zhou, X. (2022). Persuasive Schemes for Financial Exploitation in Online Romance Scam: An Anatomy on Sha Zhu Pan (杀猪盘) in China. Victims & Offenders, 18(5), 915–942. https://doi.org/10.1080/15564886.2022.2051109 Publication record
- Whitty, M. T. (2013a). Anatomy of the online dating romance scam. Security Journal, 28(4), 443–455. https://doi.org/10.1057/sj.2012.57 Publication record
- Whitty, M. T. (2013b). The Scammers Persuasive Techniques Model: Development of a Stage Model to Explain the Online Dating Romance Scam. British Journal of Criminology, 53(4), 665–684. https://doi.org/10.1093/bjc/azt009 Publication record
This AI-assisted synthesis is based on reviewed evidence records. It may contain errors or omissions. Follow the publication and DOI links, consult the original works, and make your own judgment about the evidence.
Revision history
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Version 99
Current public versionAutomated evidence-grounded revision attempt 3. Initial version.
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Version 98
Automated evidence-grounded revision attempt 2. Initial version.
- Publications: 21 (-1)
- Evidence statements: 80 (-3)
- Cited sources: 21 (-1)
Sources removed: “If U Don't Pay they will Share the Pics”: Exploring Sextortion in the Context of Romance Fraud.
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Version 97
Automated evidence-grounded revision attempt 1. Initial version of this synthesis created from the supplied reviewed evidence records; no prior-version differences to report.
- Publications: 22 (-8)
- Evidence statements: 83 (-27)
- Cited sources: 22 (-8)
Sources removed: Catfishing: a conduta de assumir uma identidade falsa nas plataformas de mídia social e suas consequências jurídico-penais; CHAPTER 10 The Online Mutual Help Practices of Romance Fraud Victims; Hook, line, and sinker: the mechanics of fraud; Online romance scams and victimhood; and 4 more.
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