  With the rapid advancement of generative AI, multimodal deepfakes, which
manipulate both audio and visual modalities, have drawn increasing public
concern. Currently, deepfake detection has emerged as a crucial strategy in
countering these growing threats. However, as a key factor in training and
validating deepfake detectors, most existing deepfake datasets primarily focus
on the visual modal, and the few that are multimodal employ outdated
techniques, and their audio content is limited to a single language, thereby
failing to represent the cutting-edge advancements and globalization trends in
current deepfake technologies. To address this gap, we propose a novel,
multilingual, and multimodal deepfake dataset: PolyGlotFake. It includes
content in seven languages, created using a variety of cutting-edge and popular
Text-to-Speech, voice cloning, and lip-sync technologies. We conduct
comprehensive experiments using state-of-the-art detection methods on
PolyGlotFake dataset. These experiments demonstrate the dataset's significant
challenges and its practical value in advancing research into multimodal
deepfake detection.
