MétaCan
Menu
Back to cohort
Record W2952169655 · doi:10.1109/sibcon.2019.8729634

Thermal Print Scanning Attacks in Theretail Environments

2019· article· en· W2952169655 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Media Forensic Detection
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsComputer scienceComputer securityPunchingKey (lock)ChipEmbedded systemEngineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

The residual heat left by fingers on a PIN pad may breach the confidentiality of the card access codes. With over five billion chip-enabled cards across the globe, thermal imaging attack may create new crime avenue. This paper studies various vectors of thermal image attacks on PIN pad terminals with the main goal to outline potential controls to prevent such attacks. Previous research work confirms that the success of attack depends upon various factors like camera angle, camera-to-PIN pad distance, time between key punching and image taken, and the room temperature. These factors have been revisited as per the potential attack scenarios in a typical retail setup to find adoptable countermeasures. The research suggested deterring and preventive controls against thermal image attack on PIN terminals with emphasis on the applicability of these controls. The control measures such as the use of on-demand virtual keyboard and redesigned curved PIN pad terminal have been studied in details as an extra layer in physical security.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.199
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicDigital Media Forensic DetectionFrench-language works237,207