Through the Lens: Benchmarking Deepfake Detectors Against Moiré-Induced Distortions

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:

https://forms.gle/oifqaoujH6q73JnR6