DeePhy: On Deepfake Phylogeny
Open accessAbstract
Deepfake refers to tailored and synthetically generated videos which are now prevalent and spreading on a large scale, threatening the trustworthiness of the information available online. While existing datasets contain different kinds of deepfakes which vary in their generation technique, they do not consider progression of deepfakes in a “phylogenetic” manner. It is possible that an existing deepfake face is swapped with another face. This process of face swapping can be performed multiple times and the resultant deepfake can be evolved to confuse the deepfake detection algorithms. Further, many databases do not provide the employed generative model as target labels. Model attribution helps in enhancing the explainability of the detection results by providing information on the generative model employed. In order to enable the research community to address these questions, this paper proposes DeePhy, a novel Deepfake Phylogeny dataset which consists of 5040 deep-fake videos generated using three different generation techniques. There are 840 videos of one-time swapped deep-fakes, 2520 videos of two-times swapped deepfakes and 1680 videos of three-times swapped deepfakes. With over 30 GBs in size, the database is prepared in over 1100 hours using 18 GPUs of 1,352 GB cumulative memory. We also present the benchmark on DeePhy dataset using six deep-fake detection algorithms. The results highlight the need to evolve the research of model attribution of deepfakes and generalize the process over a variety of deepfake generation techniques. The database is available at: http://iab-rubric.org/deephy-database
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Only a few datasets like OpenForensics, DF-Platter, and DeeShy attempt to involve challenging scenarios such as multiple fake faces in an image or video, low-quality deepfakes, or face occlusion.
“Only a few datasets like OpenForensics [31], DF-Platter [44], and DeeShy [43] attempt to involve challenging scenarios such as multiple fake faces in an image or video, low-quality DFs, or face occlusion.”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [43]
The Dataset Statistics and Annotation and Diversity sections describe DeePhy as a deepfake dataset with face occlusion, supporting it as an example of a dataset involving a challenging scenario. The citing sentence calls it “DeeShy,” whereas the cited paper names its dataset “DeePhy.” The paper does not present DeePhy as a dataset of multiple fake faces in one frame or low-quality deepfakes. (LLM, conf=0.82)
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- Authors
- Narayan, K., Agarwal, H., Thakral, K., Mittal, S., Vatsa, M., & Singh, R.
- Affiliations
- Indian Institute of Technology Jodhpur (IN)
- IIT Jodhpur,India
- Venue
- 2022 IEEE International Joint Conference on Biometrics (IJCB)
- Publisher
- IEEE
- Published
- 2022-10-10
- Pages
- 1-10
- OpenAlex
- W4316924313
- Cited by
- 26
- References
- 40
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 9 · 2023: 6
- FWCI
- 1.90
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Joint Conference on Biometrics (IJCB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, face swapping, deepfake datasets, synthetic video
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