Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors.

翻译：近期深度伪造技术的快速进步使得创建高度逼真的虚假媒体（如视频、图像和音频）成为可能。这些材料对人类身份验证构成重大挑战，例如冒用身份、传播虚假信息，甚至威胁国家安全。为了跟上这些快速进展，研究者提出了多种深度伪造检测算法，这导致了深度伪造创建者与检测者之间持续的军备竞赛。然而，这些检测器往往不可靠，且经常无法检测出深度伪造内容。本研究重点阐述了它们在深度伪造检测中面临的挑战，包括（1）伪影预处理流程，以及（2）构建防御模型时未考虑新型未知深度伪造样本生成器的问题。我们的工作揭示了该领域需要进一步研究与开发，以创建更鲁棒且更可靠的检测器。