Deepfake crimes in the age of AI: A bibliometric study of emerging risks and research trends
Bibliographic record
Abstract
This study presents a bibliometric analysis of scholarly research on deepfake crimes in the age of AI, examining emerging risks and research trends between 2019 and 2025. Drawing on 349 publications from the Web of Science Core Collection, the study uses VOSviewer to map publication patterns, collaboration networks, and thematic clusters. The results reveal that research on deepfake crimes is very interdisciplinary, as AI-based detection studies are increasingly intersecting with legal, social, and criminal discussions. The cluster analysis highlights that while significant progress has been made in technical detection methods, there are still serious gaps in addressing the societal harms of misinformation and non-consensual content. These results indicate that the field is not only growing rapidly, but is also moving towards a more integrated agenda that combines technological innovation with ethical and regulatory considerations. The study contributes theoretically by framing deep counterfeiting as technological and criminal phenomena, and practically by providing insights to policy makers, researchers and practitioners seeking to mitigate the societal and security risks of artificial media.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.100 | 0.148 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".