Through the Lens: Benchmarking Deepfake Detectors Against Moiré-Induced Distortions
Authors/Creators
-
1.
Sungkyunkwan University
-
2.
Commonwealth Scientific and Industrial Research Organisation
- 3. CSIRO Marsfield
- 4. Sungkyunkwan University - Suwon Campus
Description
Deepfake detection remains a pressing challenge, particularly in real-world settings where smartphone-captured media often introduces Moiré artifacts that can distort detection outcomes. This study systematically evaluates state-of-the-art (SOTA) deepfake detectors on Moiré-affected videos—an issue that has received little attention. We collected a dataset of 12,832 videos, spanning 35.64 hours, from CelebDF, DFD, DFDC, UADFV, and FF++ datasets, capturing footage under diverse real-world conditions, including varying screens, smartphones, lighting setups, and camera angles.
We provide the dataset request through the Google Form below:
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Additional details
Software
- Repository URL
- https://github.com/Razaib-Tariq/DeepMoireFake
- Programming language
- Python
- Development Status
- Active