---
title: "deepfakes/faceswap: papers citing this GitHub repository"
description: "27 papers in the tracked journals link to the GitHub repository deepfakes/faceswap in their Crossref records, the first in 2020; most of them (7) in 2022."
url: https://scholar4ai.com/code/github-deepfakes-faceswap
markdown_url: https://scholar4ai.com/code/github-deepfakes-faceswap.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "27 papers in the tracked journals link to the GitHub repository deepfakes/faceswap in their Crossref records, the first in 2020; most of them (7) in 2022. (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)
---

# deepfakes/faceswap: papers linking to it

Research papers in the 480 tracked journals that link to deepfakes/faceswap (https://github.com/deepfakes/faceswap), 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
- 57,599 stars
- 13,433 forks
- Python
- license GPL-3.0
- created 2017
- https://github.com/deepfakes/faceswap

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

## Key findings

- 27 papers in the tracked journals link to the GitHub repository deepfakes/faceswap in their Crossref records, the first in 2020; most of them (7) in 2022.
- By category: AI (27); the journals with the most are Expert Systems with Applications (6), Pattern Recognition (5) and International Journal of Computer Vision (5).
- 17 of the papers build on one another: a later paper cites an earlier one that also links deepfakes/faceswap.

## By year

*Papers linking to deepfakes/faceswap by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 7 |
| 2023 | 4 |
| 2024 | 6 |
| 2025 | 4 |
| 2026 | 4 |

## Journals

*Journals whose papers link to deepfakes/faceswap most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 6 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 5 |
| [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 5 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 3 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 3 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [PRRNet: Pixel-Region relation network for face forgery detection](https://doi.org/10.1016/j.patcog.2021.107950) (Pattern Recognition, 2021)
  - [Learning a deep dual-level network for robust DeepFake detection](https://doi.org/10.1016/j.patcog.2022.108832) (Pattern Recognition, 2022) — cites the paper above
    - [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
  - [Watching the BiG artifacts: Exposing DeepFake videos via Bi-granularity artifacts](https://doi.org/10.1016/j.patcog.2022.109179) (Pattern Recognition, 2022) — cites the paper above
    - [Deepfacelab: Integrated, flexible and extensible face-swapping framework](https://doi.org/10.1016/j.patcog.2023.109628) (Pattern Recognition, 2023) — cites the paper above
      - [Learning dual aggregate features for face forgery detection](https://doi.org/10.1007/s00521-024-10700-6) (Neural Computing and Applications, 2024) — cites the paper above
  - [MRT-Net: Auto-adaptive weighting of manipulation residuals and texture clues for face manipulation detection](https://doi.org/10.1016/j.eswa.2023.120898) (Expert Systems with Applications, 2023) — cites the paper above
    - [FreqFaceNet: an enhanced transformer architecture with dual-order frequency attention for deepfake detection](https://doi.org/10.1007/s10489-024-06168-5) (Applied Intelligence, 2025) — cites the paper above
- [IR-Capsule: Two-Stream Network for Face Forgery Detection](https://doi.org/10.1007/s12559-022-10008-4) (Cognitive Computation, 2022)
  - [Dual-Branch Pre-activation Bottleneck Transformer for Face Forgery Detection](https://doi.org/10.1007/s12559-025-10473-7) (Cognitive Computation, 2025) — cites the paper above
- [Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward](https://doi.org/10.1007/s10489-022-03766-z) (Applied Intelligence, 2022)
  - [An integrated spatiotemporal-based methodology for deepfake detection](https://doi.org/10.1007/s00521-022-07633-3) (Neural Computing and Applications, 2022) — cites the paper above
  - [TAN-GFD: generalizing face forgery detection based on texture information and adaptive noise mining](https://doi.org/10.1007/s10489-023-04462-2) (Applied Intelligence, 2023) — cites the paper above
- [Wavelet-packets for deepfake image analysis and detection](https://doi.org/10.1007/s10994-022-06225-5) (Machine Learning, 2022)
  - [Rethinking Open-World DeepFake Attribution with Multi-perspective Sensory Learning](https://doi.org/10.1007/s11263-024-02184-7) (International Journal of Computer Vision, 2024) — cites the paper above
- [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, 2025)
  - [CAPMamba: A hybrid vision model for enhancing generalization in deepfake detection](https://doi.org/10.1016/j.eswa.2025.130913) (Expert Systems with Applications, 2025) — cites the paper above

## Papers

*Papers linking to deepfakes/faceswap, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2022-06-03 | [Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward](https://doi.org/10.1007/s10489-022-03766-z) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Momina Masood, Marriam Nawaz, Khalid Mahmood Malik et al. | 545 | cites it |
| 2022-05-04 | [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://doi.org/10.1007/s11263-022-01606-8) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Felix Juefei-Xu, Run Wang, Yihao Huang et al. | 191 | cites it |
| 2023-04-28 | [Deepfacelab: Integrated, flexible and extensible face-swapping framework](https://doi.org/10.1016/j.patcog.2023.109628) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Kunlin Liu, Ivan Perov, Daiheng Gao et al. | 137 | cites it |
| 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 |
| 2021-03-24 | [PRRNet: Pixel-Region relation network for face forgery detection](https://doi.org/10.1016/j.patcog.2021.107950) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Zhihua Shang, Hongtao Xie, Zheng-Jun Zha et al. | 107 | cites it |
| 2022-08-31 | [Wavelet-packets for deepfake image analysis and detection](https://doi.org/10.1007/s10994-022-06225-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Moritz Wolter, Felix Blanke, Raoul Heese et al. | 69 | cites it |
| 2022-06-03 | [Learning a deep dual-level network for robust DeepFake detection](https://doi.org/10.1016/j.patcog.2022.108832) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Wenbo Pu, Jing Hu, Xin Wang et al. | 67 | cites it |
| 2023-11-23 | [An ensemble of CNNs with self-attention mechanism for DeepFake video detection](https://doi.org/10.1007/s00521-023-09196-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Karima Omar, Rasha Hassan Sakr, Mohammed F. Alrahmawy | 55 | 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 |
| 2022-11-13 | [Watching the BiG artifacts: Exposing DeepFake videos via Bi-granularity artifacts](https://doi.org/10.1016/j.patcog.2022.109179) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Han Chen, Yuezun Li, Dongdong Lin et al. | 39 | cites it |
| 2024-05-23 | [WATCHER: Wavelet-Guided Texture-Content Hierarchical Relation Learning for Deepfake Detection](https://doi.org/10.1007/s11263-024-02116-5) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Yuan Wang, Chen Chen, Ning Zhang et al. | 37 | cites it |
| 2024-06-24 | [Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection](https://doi.org/10.1007/s11263-024-02054-2) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Yuting Xu, Jian Liang, Lijun Sheng et al. | 36 | cites it |
| 2023-06-28 | [MRT-Net: Auto-adaptive weighting of manipulation residuals and texture clues for face manipulation detection](https://doi.org/10.1016/j.eswa.2023.120898) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Ankit Kumar Yadav, Dinesh Kumar Vishwakarma | 34 | cites it |
| 2020-04-19 | [Deep learning for face image synthesis and semantic manipulations: a review and future perspectives](https://doi.org/10.1007/s10462-020-09835-4) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Mahla Abdolahnejad, Peter Xiaoping Liu | 29 | cites it |
| 2023-02-16 | [TAN-GFD: generalizing face forgery detection based on texture information and adaptive noise mining](https://doi.org/10.1007/s10489-023-04462-2) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Yi Xiao Zhao, Xin Jin, Song Gao et al. | 29 | cites it |
| 2022-06-02 | [IR-Capsule: Two-Stream Network for Face Forgery Detection](https://doi.org/10.1007/s12559-022-10008-4) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Kaihan Lin, Weihong Han, Shudong Li et al. | 15 | cites it |
| 2024-08-12 | [Rethinking Open-World DeepFake Attribution with Multi-perspective Sensory Learning](https://doi.org/10.1007/s11263-024-02184-7) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Zhimin Sun, Shen Chen, Taiping Yao et al. | 12 | 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 |
| 2025-02-25 | [FreqFaceNet: an enhanced transformer architecture with dual-order frequency attention for deepfake detection](https://doi.org/10.1007/s10489-024-06168-5) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Varun Gupta, Vaibhav Srivastava, Ankit Kumar Yadav et al. | 9 | cites it |
| 2024-08-13 | [Test-time Forgery Detection with Spatial-Frequency Prompt Learning](https://doi.org/10.1007/s11263-024-02208-2) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Junxian Duan, Yuang Ai, Jipeng Liu et al. | 8 | cites it |
| 2026-04-23 | [Towards few-shot deepfake detection with an enhanced CLIP model](https://doi.org/10.1016/j.neunet.2026.109027) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Yumin Yang, Xueyi Zhang, Bo Yan et al. | 5 | cites it |
| 2025-06-01 | [Dual-Branch Pre-activation Bottleneck Transformer for Face Forgery Detection](https://doi.org/10.1007/s12559-025-10473-7) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Kaihan Lin, Jujian Lv, Huimin Zhao et al. | 3 | cites it |
| 2026-04-03 | [Refining forgery-aware prompts for deepfake detection with pattern blended samples](https://doi.org/10.1016/j.patcog.2026.113659) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Caibo Feng, Chunxiao Liu | 2 | cites it |
| 2024-11-29 | [Learning dual aggregate features for face forgery detection](https://doi.org/10.1007/s00521-024-10700-6) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Yuru Kou, Qian Jiang, Jun Zhang et al. | 1 | cites it |
| 2025-12-21 | [CAPMamba: A hybrid vision model for enhancing generalization in deepfake detection](https://doi.org/10.1016/j.eswa.2025.130913) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Shudong Li, Yilin Feng, Xiaobo Wu et al. | 1 | cites it |
| 2026-06-01 | [DDSF-Net: dual-domain deepfake detection via semantic suppression and spatial-frequency mapping](https://doi.org/10.1016/j.eswa.2026.133102) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Zhifeng Xing, Li Liu, Yingchun Wu et al. | 1 | cites it |
| 2026-04-23 | [STCDePhysio : A decoupled deepfake detection framework based on spatio-temporal consistency of human physiological signals](https://doi.org/10.1016/j.eswa.2026.132568) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Jue Tian, Le Zhang, Yang Liu et al. | 0 | 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). deepfakes/faceswap: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-deepfakes-faceswap

```bibtex
@misc{scholar4ai-code-github-deepfakes-faceswap,
  author = {{Scholar4AI}},
  title = {deepfakes/faceswap: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-deepfakes-faceswap}},
  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/).