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高度逼真的虚假媒体（如视频、图像和音频）得以生成。这些材料对人类的身份验证构成重大挑战，例如导致身份冒用、传播虚假信息，甚至威胁国家安全。为跟上这些快速发展的步伐，研究者提出了多种深度伪造检测算法，从而在深度伪造生成者与检测者之间形成了持续的军备竞赛。然而，这些检测器往往不可靠，经常无法成功检测出深度伪造内容。本研究揭示了它们在检测深度伪造时所面临的挑战，包括：（1）预处理流程中的人工痕迹问题；（2）在构建防御模型时未考虑那些尚未见过的、由新型生成器产生的深度伪造样本。本文工作凸显了在该领域开展进一步研发以构建更鲁棒、更可靠的检测器的必要性。