  Deepfake detection is critical in mitigating the societal threats posed by
manipulated videos. While various algorithms have been developed for this
purpose, challenges arise when detectors operate externally, such as on
smartphones, when users take a photo of deepfake images and upload on the
Internet. One significant challenge in such scenarios is the presence of
Moir\'e patterns, which degrade image quality and confound conventional
classification algorithms, including deep neural networks (DNNs). The impact of
Moir\'e patterns remains largely unexplored for deepfake detectors. In this
study, we investigate how camera-captured deepfake videos from digital screens
affect detector performance. We conducted experiments using two prominent
datasets, CelebDF and FF++, comparing the performance of four state-of-the-art
detectors on camera-captured deepfake videos with introduced Moir\'e patterns.
Our findings reveal a significant decline in detector accuracy, with none
achieving above 68% on average. This underscores the critical need to address
Moir\'e pattern challenges in real-world deepfake detection scenarios.
