---
title: "dfaker/df: papers citing this GitHub repository"
description: "4 papers in the tracked journals link to the GitHub repository dfaker/df in their Crossref records, the first in 2021; most of them (1) in 2025."
url: https://scholar4ai.com/code/github-dfaker-df
markdown_url: https://scholar4ai.com/code/github-dfaker-df.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the GitHub repository dfaker/df in their Crossref records, the first in 2021; most of them (1) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# dfaker/df: papers linking to it

Research papers in the 480 tracked journals that link to dfaker/df (https://github.com/dfaker/df), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 485 stars
- 133 forks
- Python
- license MPL-2.0
- created 2018
- archived
- https://github.com/dfaker/df

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the GitHub repository dfaker/df in their Crossref records, the first in 2021; most of them (1) in 2025.
- By category: AI (3) and Information Systems (1); the journals with the most are Expert Systems with Applications (2), International Journal of Information Management Data Insights (1) and Neural Computing and Applications (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links dfaker/df.

## By year

*Papers linking to dfaker/df by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2022 | 1 |
| 2024 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to dfaker/df most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [International Journal of Information Management Data Insights](https://scholar4ai.com/journals/international-journal-of-information-management-data-insights.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Exposing deepfakes using a deep multilayer perceptron – convolutional neural network model](https://doi.org/10.1016/j.jjimei.2021.100054) (International Journal of Information Management Data Insights, 2021)
  - [Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence](https://doi.org/10.1016/j.eswa.2024.124260) (Expert Systems with Applications, 2024) — cites the paper above

## Papers

*Papers linking to dfaker/df, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-05-18 | [Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence](https://doi.org/10.1016/j.eswa.2024.124260) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Fakhar Abbas, Araz Taeihagh | 116 | cites it |
| 2021-12-22 | [Exposing deepfakes using a deep multilayer perceptron – convolutional neural network model](https://doi.org/10.1016/j.jjimei.2021.100054) | [International Journal of Information Management Data Insights](https://scholar4ai.com/journals/international-journal-of-information-management-data-insights.md) | Santosh Kolagati, T. Chindrella Priyadharshini, V. Mary Anita Rajam | 88 | cites it |
| 2022-08-02 | [An integrated spatiotemporal-based methodology for deepfake detection](https://doi.org/10.1007/s00521-022-07633-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Aya Ismail, Marwa S. Elpeltagy, Mervat S. Zaki et al. | 42 | cites it |
| 2025-06-14 | [FAME: a lightweight spatio-temporal network for model attribution of face-swap deepfakes](https://doi.org/10.1016/j.eswa.2025.128571) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Wasim Ahmad, Yan‐Tsung Peng, Yuan-Hao Chang | 10 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). dfaker/df: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-dfaker-df

```bibtex
@misc{scholar4ai-code-github-dfaker-df,
  author = {{Scholar4AI}},
  title = {dfaker/df: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-dfaker-df}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).