PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset

Autor:	Hou, Yang, Fu, Haitao, Chen, Chuankai, Li, Zida, Zhang, Haoyu, Zhao, Jianjun
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Audio and Speech Processing 68T45 I.4.9
Druh dokumentu:	Working Paper
Popis:	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detectors, most existing deepfake datasets primarily focus on the visual modal, and the few that are multimodal employ outdated techniques, and their audio content is limited to a single language, thereby failing to represent the cutting-edge advancements and globalization trends in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. Comment: 13 page, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.08838 Zobrazit plný text záznamu View this record from Arxiv