Evidence map›Paper›PMID 41617818›Full record

ArticleScientific reports2026

Mitigating shoulder spoofing vulnerabilities in mobile payment systems: a security framework.

Omar Alqahtani, M R Dileep, Mohamed Ghouse, S D Vidya Sagar, Sreekanth Rallapalli, Ajit Danti

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Omar AlqahtaniDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, KSA, Saudi Arabia.ORCID http://orcid.org/0009-0001-6652-3512
M R DileepDepartment of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Nitte (Deemed to be University), Bengaluru, Karnataka, India. dileep.mr@nmit.ac.in.ORCID http://orcid.org/0000-0002-0010-7276
Mohamed GhouseDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, KSA, Saudi Arabia.ORCID http://orcid.org/0000-0001-9202-8374
S D Vidya SagarDepartment of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Nitte (Deemed to be University), Bengaluru, Karnataka, India.ORCID http://orcid.org/0000-0002-9390-7810
Sreekanth RallapalliDepartment of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Nitte (Deemed to be University), Bengaluru, Karnataka, India.ORCID http://orcid.org/0000-0002-1626-0320
Ajit DantiDepartment of Artificial Intelligence, College of Technology, City University Ajman, Ajman, UAE.ORCID http://orcid.org/0000-0002-2462-1771

Funding

Cyber Security Research and Innovation Pioneers Initiative, provided by the National Cyber Security Authority (NCA) in the Kingdom of Saudi Arabia CRPG - 25 - 3170
6 · The paper itself

Abstract

The rapid growth of mobile payment platforms has enhanced transactional convenience but also introduced critical security challenges, notably shoulder spoofing. This attack occurs when unauthorized individuals or surveillance devices visually intercept sensitive information, such as Mobile Personal Identification Numbers (MPINs), during payment processes. Existing security mechanisms-including PIN masking and screen dimming-fail to detect environmental threats or provide adaptive responses, leaving users vulnerable in public spaces. To address this gap, propose a novel solution titled Gaze-Aware Threat Detection with Contextual Scene Analysis (GATCSA). GATCSA leverages the front-facing camera and on-device computer vision algorithms to monitor the surroundings during mobile transactions. The system identifies suspicious behavior such as gaze fixation by nearby individuals or the presence of surveillance equipment targeting the mobile screen. A risk evaluation module considers proximity, gaze direction, and focus duration to classify threat levels in real time. Upon detection, the system provides users with contextual alerts and actionable suggestions-such as changing the device angle, enabling a privacy screen, or halting the transaction-to safeguard against unauthorized visual access. Unlike traditional methods, GATCSA processes all data locally to ensure user privacy and operates efficiently on resource-constrained mobile devices. Preliminary testing in varied real-world conditions-differing in lighting, crowd density, and device orientation-demonstrates high accuracy in threat identification and user responsiveness. By integrating gaze tracking with environmental awareness, GATCSA represents a significant advancement in mobile payment security, enhancing user trust and privacy during digital transactions.

Indexed as

Computer visionGaze detectionMobile payment securityReal-Time threat analysisShoulder spoofing

Identifiers

PMID41617818
PMCPMC12913985

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.