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.

翻译：深度伪造检测仍是一项紧迫挑战，尤其是在现实场景中，从数字屏幕通过智能手机捕获的媒体常引入莫尔条纹伪影，可能扭曲检测结果。本研究系统性地评估了最先进的深度伪造检测器在处理受莫尔条纹影响的视频上的表现，这一问题此前鲜受关注。我们收集了来自Celeb-DF、DFD、DFDC、UADFV和FF++数据集的12,832个视频，总时长35.64小时，涵盖了多样化的现实条件，包括不同屏幕、智能手机、光照设置和拍摄角度。为深入探究莫尔条纹模式对深度伪造检测的影响，我们使用自建的DeepMoiréFake（简称DMF）数据集及两种合成莫尔条纹生成技术进行了额外实验。在15个表现最佳的检测器中，结果显示莫尔条纹伪影使性能下降高达25.4%，而合成生成的莫尔条纹模式导致准确率降低21.4%。出乎意料的是，旨在缓解该问题的去莫尔条纹方法反而加剧了问题，使准确率最多下降17.2%。这些发现强调了检测模型亟需能稳健处理莫尔条纹失真及其他现实挑战（如压缩、锐化和模糊）的能力。通过引入DMF数据集，我们旨在推动未来研究弥合受控实验与实际深度伪造检测之间的差距。