Through the Lens: Benchmarking Deepfake Detectors Against Moir'e-Induced Distortions

Deepfake detection remains a pressing challenge, particularly in real-world settings where smartphone-captured media from digital screens often introduces Moir'e artifacts that can distort detection outcomes. This study systematically evaluates state-of-the-art (SOTA) deepfake detectors on Moir'e-affected videos, an issue that has received little attention. We collected a dataset of 12,832 videos, spanning 35.64 hours, from the Celeb-DF, DFD, DFDC, UADFV, and FF++ datasets, capturing footage under diverse real-world conditions, including varying screens, smartphones, lighting setups, and camera angles. To further examine the influence of Moir'e patterns on deepfake detection, we conducted additional experiments using our DeepMoir'eFake, referred to as (DMF) dataset and two synthetic Moir'e generation techniques. Across 15 top-performing detectors, our results show that Moir'e artifacts degrade performance by as much as 25.4%, while synthetically generated Moir'e patterns lead to a 21.4% drop in accuracy. Surprisingly, demoir'eing methods, intended as a mitigation approach, instead worsened the problem, reducing accuracy by up to 17.2%. These findings underscore the urgent need for detection models that can robustly handle Moir'e distortions alongside other realworld challenges, such as compression, sharpening, and blurring. By introducing the DMF dataset, we aim to drive future research toward closing the gap between controlled experiments and practical deepfake detection.