探究莫尔图案对Deepfake检测器的影响
Exploring the Impact of Moire Pattern on Deepfake Detectors
AI总结：
本研究探究莫尔图案对Deepfake检测器的影响，通过CelebDF、FF++数据集实验发现，含莫尔图案的屏拍Deepfake视频会使四类先进检测器准确率降至平均68%以下，凸显解决该问题的必要性。
摘要：
Deepfake检测对于缓解篡改视频带来的社会威胁至关重要。尽管人们已为此开发了多种算法，但当检测器在外部环境（例如智能手机上）运行，即用户拍摄Deepfake图像的照片并上传至互联网时，会出现诸多挑战。此类场景中的一个重大挑战是莫尔图案（Moiré pattern）的存在，它会降低图像质量，干扰包括深度神经网络（DNN）在内的传统分类算法。莫尔图案对Deepfake检测器的影响目前仍 largely 未被探究。在本研究中，我们调查了从数字屏幕拍摄的相机捕获Deepfake视频如何影响检测器性能。我们使用两个知名数据集CelebDF和FF++开展实验，对比了四种最先进的检测器在引入莫尔图案的相机捕获Deepfake视频上的性能。我们的研究结果显示，检测器准确率出现显著下降，没有一种检测器的平均准确率能达到68%以上。这凸显了在真实世界Deepfake检测场景中解决莫尔图案挑战的迫切需求。
英文摘要：
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 Moiré patterns, which degrade image quality and confound conventional classification algorithms, including deep neural networks (DNNs). The impact of Moiré 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 Moiré patterns. Our findings reveal a significant decline in detector accuracy, with none achieving above 68% on average. This underscores the critical need to address Moiré pattern challenges in real-world deepfake detection scenarios.
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