Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors. In this paper, we examine the need for high-quality samples in the training datasets of such detectors. Accordingly, we show that deepfake detectors proven to generalize well on multiple research datasets still struggle in real-world scenarios with well-crafted fakes. First, we propose a novel autoencoder for face swapping alongside an advanced face blending technique, which we utilize to generate 90 high-quality deepfakes. Second, we feed those fakes to a state-of-the-art detector, causing its performance to decrease drastically. Moreover, we fine-tune the detector on our fakes and demonstrate that they contain useful clues for the detection of manipulations. Overall, our results provide insights into the generalization of deepfake detectors and suggest that their training datasets should be complemented by high-quality fakes since training on mere research data is insufficient.

翻译：随着深度伪造对安全和隐私的威胁日益加剧，开发鲁棒且可靠的检测器至关重要。本文研究了检测器训练数据集中高质量样本的必要性。据此，我们证明，在多个研究数据集上被证明具有良好的泛化能力的深度伪造检测器，在面对精心制作的伪造内容时仍难以应对真实场景。首先，我们提出了一种新型的人脸交换自编码器，结合高级人脸融合技术，生成了90个高质量深度伪造样本。其次，我们将这些伪造样本输入最先进的检测器，导致其性能显著下降。此外，我们对检测器进行微调，证明这些伪造样本包含检测篡改的有用线索。总体而言，我们的研究结果揭示了深度伪造检测器的泛化特性，表明仅基于研究数据训练不足，其训练数据集应辅以高质量伪造样本。