Enhancing Digital Investigation: The Role of Generative AI (ChatGPT) in Evidence Identification and Analysis in Digital Forensics
Bibliographic record
Abstract
Generative Artificial Intelligence (GAI) has garnered considerable attention across disciplines such as science, digital forensics, and literature. Advanced large language modeling systems (LLMs), including ChatGPT and Large Language Model Meta AI (LLaMA), have become essential tools in digital forensics due to their sophisticated Natural Language Processing (NLP) capabilities. These systems enable efficient processing of extensive text datasets, sentiment analysis, and real-time threat detection. This research explores the effectiveness of AI-driven methods in digital forensics by conducting comprehensive tests on various applications, such as artifact comprehension, evidence search, and incident response. The results confirm the transformative role of ChatGPT in enhancing the speed and accuracy of investigation, as the study showed that the system can analyze data and images related to crimes and provide comprehensive reports. Not only does he process evidence, but it can also extract complete conversations related to crime, determining when it occurred and whether it was planned. The system meticulously analyzes the data sent to it to provide additional details such as possible motives and behavior of suspects. It provides investigators with an in-depth understanding that can be used at various stages of the investigation, and even in the courts as credible evidence. This approach reflects the importance of responsible adoption of the system while offering guidelines for its responsible adoption and future development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".