Counterfeit Detection in the e-Commerce Industry Using Machine Learning: A Review
Bibliographic record
Abstract
The past decade has experienced an exponential rise in online purchases along with using credit cards and associated financial tools. This widespread e-commerce use has resulted in an unprecedented surge in frauds that range from financial frauds to fake online-shop frauds. Consequently. The detection and safeguarding of users from such frauds have been a vital goal to achieve for many organizations and enterprises, most of which aim to achieve the same through the application of machine learning to build classifier models that detect and classify data (transactions, online shops, and other e-commerce data) into fraudulent and legit classes. This survey paper aims to understand the advancements made in the last decade in the field to understand the progress made along with the gaps associated with the current research work. Moreover, the hurdles or challenges pertaining to widespread implementation are also discussed with potential solutions and prospects comprehensively; while providing insights on the most feasible ML algorithm(s) based on the survey, followed by future directions of research work to make it equipped for real-world implementation.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".