Scam Guard: Intelligent Scam Protection for Users
Bibliographic record
Abstract
With the growing use of messaging platforms like WhatsApp, users face more harmful content such as phishing links, malware, fake media, and inappropriate language. This paper introduces Scam Guard, a lightweight and non-intrusive system for Android that protects users from these threats in real-time. It uses Android's Accessibility Service to monitor incoming messages and conduct multi-layered analysis, including keyword detection, regular expression-based link scanning, and integration with external threat intelligence APIs like VirusTotal. It also checks media files for forgery using image analysis techniques and hides harmful or sensitive content from the screen to limit user exposure. Scam Guard classifies messages as safe, questionable, or dangerous. It quickly alerts users with contextual notifications, providing options to block the sender or dismiss the message. The system works without needing root access, which helps preserve user privacy and device integrity. Key components include the Accessibility Service, Notification Manager, Broadcast Receivers, and a Contact Management Module. By providing a real-time, privacy-focused security solution, Scam Guard aims to improve user safety against modern scam tactics on messaging apps.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".