Mobile Forensic Tools for Digital Crime Investigation: Comparison and Evaluation
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.
Bibliographic record
Abstract
The advancement of new technology is quickening.Because of the features and applications available on mobile devices, smartphones are gradually taking over the role of computers.One of them is a multi-platform instant messaging application with various features that can bring people together, but the negative aspect is that it is used to commit digital crimes.Digital evidence is required in the investigation of digital crimes, In order to obtain digital evidence, a set of forensic tools is required to carry out the forensic process of physical evidence.The goal of this research is to describe and contrast the forensic process.These tools are currently based on digital evidence obtained through the stages of the Digital Forensic Research Workshop.MEF, DB4S, OFD, and FMF are the forensic tools used in this study.According to the findings, FMF has the highest extraction capability for obtaining digital evidence, OFD has advantages in terms of data acquisition features, and MFE has advantages in identification, physical evidence preservation, and cloning.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 it