The generalisation capabilities to cross and unknown scenarios are one of the main issues to solve in deepfake detection research. This work provides a new face-swap dataset generated by six algorithms, such as *Blendswap, Ghost, Simswap, Uniface, Hyperswap and Inswapper, which can be used to enhance current classifiers. Our work focuses on tackling open challenges in current datasets, such as a small number of bona fide images, a limited number of subjects, and unevenly split datasets. Our new dataset consists of around 70k high-resolution (1024×1024), subject-disjoint face images, lossy-compressed and face-swapped.
Download and Copyright:
The database will be made available to other researchers through a previously signed and licensed agreement. Interested researchers can download this database by contacting juan.tapia-farias@h-da.de and philipp.srock@h-da.de
Commercial use of the Licensed Materials is strictly prohibited without prior written permission from the Licensor. A partnership will be possible, but it requires prior coordination.
All technical reports and papers that present experimental results from this database should include an acknowledgement and a reference to [1].
(*) The face-swap algorithms must be required directly from the source.
References
[1] Philipp Srock, Juan E. Tapia, Christoph Busch: HQSwap: A Challenging High-Resolution Face-Swap Dataset for Improved Manipulation Detection, 14th International Workshop on Biometrics and Forensics Côte d’Azur, EURECOM, April 23–24, 2026. Github