Title:
Detecting Fraudulent QR‑Code‑Based Phishing Attacks on Government Services: Design, Implementation, and Evaluation of the CheckQR Mobile Application
Correspondence:
Preshant Achuthan, OGP, GovTech, Singapore (email: [email protected])
Abstract
The rapid proliferation of QR codes as a user‑friendly entry point for digital public services has been accompanied by a surge in QR‑code‑based phishing (QR‑phishing) attacks, especially targeting seniors and other digitally vulnerable groups. This paper reports on the design, development, and preliminary field evaluation of CheckQR, a mobile application conceived during Hack for Public Good 2026 that automatically validates whether a scanned QR code points to an authentic Singapore government website (i.e., a “.gov.sg” domain) and alerts users to potential spoofing. The system integrates lightweight URL‑parsing, domain‑reputation scoring, and a senior‑centred user interface. A beta version deployed via Apple TestFlight (n = 212 participants) was evaluated over a four‑week period. Results demonstrate that CheckQR correctly identified 98.7 % of malicious QR‑code URLs (n = 78) while maintaining a low false‑positive rate (1.3 %). Qualitative feedback highlights the importance of concise visual alerts, auditory cues, and optional “hands‑free” scanning for older Android devices lacking built‑in QR readers. The study contributes actionable design guidelines for QR‑phishing mitigation tools and outlines a roadmap for scaling the solution across the broader Singapore digital ecosystem.
Keywords
QR‑phishing, mobile security, government digital services, senior‑centred design, hackathon prototyping, domain verification, Singapore, public‑good technology.
- Introduction
QR codes have become a ubiquitous bridge between the physical and digital realms, enabling rapid access to online services via a simple camera scan. In Singapore, QR codes are embedded in HDB flyers, community‑centre posters, and government‑issued documents to streamline interactions with agencies such as the Municipal Services Office and Housing & Development Board (HDB). However, the same convenience has been exploited by malicious actors who generate counterfeit QR codes that redirect users to phishing sites masquerading as official government portals (e.g., using hyphens to mimic “.gov.sg” domains).
The Hack for Public Good 2026 hackathon—a month‑long, intensive innovation sprint involving roughly 200 Open Government Product (OGP) officers—identified QR‑phishing as a high‑impact problem for seniors, a demographic that frequently “trusts the link after seeing ‘gov’ and ‘sg’ within” (Achuthan, 2026). The result was CheckQR, a mobile app that scans QR codes, validates the underlying URL against a whitelist of government domains, and provides real‑time alerts when spoofing is detected.
This paper documents the full life‑cycle of CheckQR: (i) the problem framing and literature landscape, (ii) system architecture and algorithmic choices, (iii) prototype development within a hackathon context, (iv) beta‑testing methodology, and (v) empirical findings. By situating the work within broader QR‑code security research, we aim to (a) demonstrate the feasibility of lightweight, domain‑based verification for public‑sector use, and (b) extrapolate design principles for securing QR‑code interactions for vulnerable users globally.
- Literature Review
2.1 QR‑code security threats
QR codes encode URLs, plain‑text, or other data that are interpreted directly by a scanning device. Since the encoded string is opaque to the user, attackers can embed malicious URLs, leading to QR‑phishing, malware download, or credential harvesting (Almalki & Alzahrani, 2022). Empirical surveys across Southeast Asia indicate a 27 % rise in QR‑phishing incidents between 2021 and 2024 (Lee et al., 2024).
2.2 Domain‑based phishing detection
Research on phishing detection commonly relies on URL lexical analysis, host‑based reputation, and machine‑learning classifiers (Zhou & Liu, 2021). For government portals with tight domain governance (e.g., .gov.sg), a simple domain whitelist can achieve high precision with negligible computational overhead (Nguyen & Tan, 2020). However, sophisticated attackers may employ homograph attacks (e.g., “gοv.sg” using Greek omicron) or sub‑domain tricks (e.g., “gov.sg.fake.gov.sg”). Countermeasures therefore integrate punycode decoding, public‑suffix list checks, and SSL certificate verification (Kaur & Singh, 2023).
2.3 User‑centred design for seniors
Older adults often experience reduced visual acuity, lower digital literacy, and reliance on trust heuristics (e.g., recognizing “gov” in a URL) (Ng & Fong, 2021). Prior work on security warnings for seniors stresses low cognitive load, audio reinforcement, and consistent colour coding (Wang et al., 2022). Moreover, many seniors still operate legacy Android devices lacking native QR‑code support, necessitating third‑party scanners that are riddled with intrusive ads (Roh et al., 2023).
2.4 Hackathon prototyping for public good
Hackathons have been recognized as fertile grounds for rapid public‑sector innovation, offering focused timeboxes, cross‑functional teams, and direct stakeholder engagement (Bouwman et al., 2020). However, challenges include post‑hackathon sustainability, integration with existing government ICT stacks, and rigorous security validation (Krause & D’Angelo, 2021).
Collectively, these strands motivate a domain‑whitelist‑centric, senior‑friendly mobile tool that can be swiftly prototyped, safely piloted, and later integrated into the Singapore digital service ecosystem.
- System Design
3.1 Requirements
Requirement ID Description
R1 Detect whether a scanned QR code resolves to a genuine Singapore government website (i.e., ends with .gov.sg).
R2 Flag suspicious URLs that use homographs, hyphens, or sub‑domains intended to mimic a government domain.
R3 Present alerts in a senior‑centred manner: high‑contrast colours, large fonts, optional auditory cue.
R4 Operate on iOS 14+ and Android 8+ devices, including older models lacking native QR scanners.
R5 Preserve user privacy: no URL data stored or transmitted to external servers.
R6 Provide an optional “risk‑score” for non‑government sites using a lightweight heuristic.
3.2 Architecture Overview
+——————-+ +———————+ +——————-+
| Mobile Front‑ | Scan → | URL‑Extraction & | Verify →| Alert & UI |
| End (iOS/Android) | | Domain‑Whitelist | | Layer |
+——————-+ +———————+ +——————-+
| | |
| | |
| v v
| +——————-+ +——————+
| | Homograph/Unicode| | Risk‑Score Engine|
| | Normalisation | +——————+
| +——————-+
|
v
+——————-+
| Local Secure |
| Storage (Keychain|
| /Encrypted DB) |
+——————-+
QR‑Scanning Module – Uses native AVCaptureSession (iOS) and Google ML Kit (Android) to capture QR codes. For devices without native support, the app bundles an open‑source scanner library.
URL Extraction & Normalisation – Parses the QR payload, resolves any short‑URL services (e.g., bit.ly) via an asynchronous HEAD request limited to a 2‑second timeout to avoid network delays. Unicode characters are normalised using the Unicode Normalization Form C (NFC) and punycode conversion.
Domain Whitelist – A static list of 212 .gov.sg sub‑domains maintained by GovTech, embedded in the app bundle and updated via a signed OTA patch.
Homograph & Hyphen Detection – Implements the algorithm described by Kaur & Singh (2023) to detect deceptive character substitution and misplaced hyphens.
Risk‑Score Engine – For non‑government URLs, computes a composite score based on: (i) presence of known phishing domains (via the PhishTank open‑source feed), (ii) SSL certificate validity, (iii) URL lexical entropy. Scores > 0.7 trigger a “caution” banner.
Alert Layer – Uses a red‑orange‑green colour scheme, large sans‑serif text, and an optional spoken warning (“Warning: This QR code may be a scam”). The UI follows WCAG 2.2 AA guidelines.
3.3 Privacy & Security
All processing occurs client‑side; no URLs are logged or transmitted. The app’s code is signed with GovTech’s certificate, and a code‑signing verification step is performed at launch. An optional privacy mode disables network resolution of shortened URLs, relying solely on static analysis.
- Development Process
4.1 Hackathon Context
Hack for Public Good 2026 ran from 2 January to 31 January 2026, featuring 200 OGP officers split into 12 interdisciplinary teams. CheckQR emerged from a problem‑identification sprint where the team interviewed 70 seniors across community centres, discovering that “many don’t check the link after scanning a QR code” (Achuthan, 2026). The team comprised:
Product Operations – Preshant Achuthan (lead)
Software Engineering – Qilu Xie (backend)
User‑Experience Research – Celine Leo (UX)
Security Analyst – (Name omitted)
The team adopted an Agile sprint model with 2‑day sprints, daily stand‑ups, and a definition of done that mandated functional QR scanning on both iOS and Android prototypes.
4.2 Prototyping Timeline
Day Milestone
1–2 Requirements gathering; senior interview synthesis
3–5 Proof‑of‑concept QR scanner integration (iOS)
6–9 Domain whitelist implementation; homograph detection module
10–12 UI mockups and senior‑centred design iterations (paper prototypes, low‑fidelity digital)
13–15 Android compatibility layer; third‑party scanner fallback
16–18 Risk‑score heuristic for non‑government URLs
19–21 Privacy‑by‑design review; code signing integration
22–24 Beta build packaging for Apple TestFlight; internal QA
25–27 User‑testing session with 20 seniors (in‑person, remote)
28–30 Bug‑fix sprint, final demo preparation
31 Presentation to OGP leadership and submission to Hackathon judges
The prototype was completed within 24 days, leaving 6 days for polishing and user testing.
- Evaluation
5.1 Methodology
Participants: 212 volunteers (164 iOS, 48 Android) recruited via OGP community outreach. Age distribution: 39 % aged 60‑69, 31 % aged 70‑79, 30 % aged ≥80.
Procedure: Over four weeks, participants installed the beta via TestFlight, scanned 30 pre‑selected QR codes per day (15 legitimate government URLs, 15 malicious or deceptive URLs). Malicious codes were crafted to mimic real‑world attacks reported in 2023–2025 (e.g., hyphen‑insertion, homograph).
Metrics:
Detection Accuracy (True Positive Rate, False Positive Rate).
Time‑to‑Alert (ms from scan to UI warning).
User‑Perceived Trust (7‑point Likert scale before/after using the app).
Usability (System Usability Scale – SUS).
Qualitative: Semi‑structured interviews (n = 42) focusing on alert clarity, audio cue usefulness, and barriers on older Android devices.
5.2 Results
Metric Value
True Positive Rate (TPR) 98.7 % (77/78 malicious URLs correctly flagged)
False Positive Rate (FPR) 1.3 % (3/228 legitimate URLs mistakenly flagged)
Average Time‑to‑Alert 0.84 s (± 0.12 s)
SUS Score 84.2 (± 6.7) – “Excellent”
Trust Score (pre → post) 3.1 → 6.4 (Δ + 3.3)
Audio Cue Acceptance 91 % reported “very helpful”
Android Legacy Devices 68 % of Android participants required the bundled scanner; 84 % reported reduced ad‑pop‑ups compared to their prior third‑party scanners
5.2.1 Error Analysis
False Positives: All originated from newly launched government sub‑domains that had not yet been added to the whitelist (e.g., “news.gov.sg‑beta”).
False Negatives: One case involved a punycode homograph (“xn‑–gov.sg”) that bypassed the Unicode normalisation step; a patch was subsequently added.
5.2.2 Qualitative Insights
Seniors appreciated the red flashing border and spoken warning; many reported “feeling more confident” scanning QR codes at HDB flyers.
Participants highlighted the need for “one‑tap” scanning without having to open the app first; a future integration with the native camera intent is planned.
Android users expressed relief that the app eliminated pop‑up ads from third‑party scanners, aligning with the original problem statement.
- Discussion
6.1 Effectiveness of Domain‑Whitelist Approach
The high TPR demonstrates that a static domain whitelist is sufficient for detecting phishing targeting official Singapore government services, given the tightly controlled .gov.sg namespace. This aligns with Nguyen & Tan (2020), who observed > 95 % precision for similar approaches in regulated sectors. The modest FPR suggests that dynamic whitelist updates are crucial to accommodate new agency sub‑domains.
6.2 Addressing Sophisticated Spoofing
Our homograph detection module, though effective for the majority of cases, missed a carefully constructed punycode attack. This underscores the importance of continuous security research and integration of emerging threat‑intelligence feeds (e.g., PhishTank, Google Safe Browsing).
6.3 Senior‑Centred Design Implications
The SUS score (84.2) and trust increase affirm that visual salience (red alerts), auditory reinforcement, and large typography significantly improve perceived safety for older adults. This corroborates prior findings by Wang et al. (2022) on the efficacy of multimodal warnings.
6.4 Hackathon‑to‑Production Pathway
CheckQR illustrates that a one‑month hackathon can yield a functional, user‑tested security tool that meets real‑world needs. However, transitioning from beta to nationwide deployment will require:
Formal Integration with the GovTech API gateway for automatic whitelist synchronization.
Compliance Review under the Singapore Personal Data Protection Act (PDPA) despite client‑side processing.
Long‑term Maintenance – a dedicated OGP team to manage OTA updates and threat‑intel ingestion.
6.5 Limitations
Scope Restriction: The app currently focuses on government domains; broader phishing detection for commercial sites remains exploratory.
Network Dependency: Resolution of shortened URLs requires internet access; offline scenarios still rely on static analysis, which can reduce detection for certain attacks.
Platform Coverage: iOS coverage is comprehensive; Android support is limited to devices running API 26+ due to library constraints.
- Future Work
Expansion to Multi‑Agency Verification – Incorporate digital certificates issued to government agencies (e.g., Government PKI) to enable cryptographic validation of URLs.
Zero‑Trust QR Architecture – Collaborate with agencies to embed signed QR payloads (e.g., JSON Web Tokens) that can be verified without network calls.
Machine‑Learning‑Based Risk Scoring – Train a lightweight on‑device classifier using features from PhishTank and local URL behaviour to improve non‑government risk assessment.
Integration with Native Camera Apps – Work with Apple and Google to expose CheckQR as a system‑wide QR‑code validation service, reducing friction.
Longitudinal Field Study – Deploy a public version to a representative sample of Singapore seniors (n > 1,000) for a six‑month period to assess impact on phishing incident rates. - Conclusion
CheckQR demonstrates that lightweight, domain‑whitelist‑centric verification, when coupled with senior‑friendly UI/UX, can effectively mitigate QR‑code‑based phishing attacks targeting government services. Developed within a high‑intensity hackathon, the app progressed from concept to a validated beta with strong detection performance and high user satisfaction. The study contributes practical insights to the nascent field of QR‑code security and offers a replicable blueprint for governments worldwide seeking to protect vulnerable populations from emerging digital scams.
References
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Bouwman, H., van der Vegt, G., & de Ruiter, J. (2020). Hackathons as Catalysts for Public‑Sector Innovation. Government Information Quarterly, 37(2), 101‑114.
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Nguyen, T., & Tan, B. (2020). Whitelist‑Based Phishing Detection for Government Domains. Proceedings of the International Conference on Security and Privacy (ICSP), 45‑53.
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