most citedExploring the Role of Audio in Multimodal Misinformation Detection

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MM20241 cited

Exploring the Role of Audio in Multimodal Misinformation Detection

Moyang Liu, Yukun Liu, Ruibo Fu +4

With the rapid development of deepfake technology, especially the deep audio fake technology, misinformation detection on the social media scene meets a great challenge. Social med…

eess.AS2024

ASRRL-TTS: Agile Speaker Representation Reinforcement Learning for Text-to-Speech Speaker Adaptation

Ruibo Fu, Xin Qi, Zhengqi Wen +10

Speaker adaptation, which involves cloning voices from unseen speakers in the Text-to-Speech task, has garnered significant interest due to its numerous applications in multi-media…

eess.AS2024

MINT: a Multi-modal Image and Narrative Text Dubbing Dataset for Foley Audio Content Planning and Generation

Ruibo Fu, Shuchen Shi, Hongming Guo +12

Foley audio, critical for enhancing the immersive experience in multimedia content, faces significant challenges in the AI-generated content (AIGC) landscape. Despite advancements…

cs.SD2024

Genuine-Focused Learning using Mask AutoEncoder for Generalized Fake Audio Detection

Xiaopeng Wang, Ruibo Fu, Zhengqi Wen +9

The generalization of Fake Audio Detection (FAD) is critical due to the emergence of new spoofing techniques. Traditional FAD methods often focus solely on distinguishing between g…

cs.SD20241 cited

Generalized Fake Audio Detection via Deep Stable Learning

Zhiyong Wang, Ruibo Fu, Zhengqi Wen +9

Although current fake audio detection approaches have achieved remarkable success on specific datasets, they often fail when evaluated with datasets from different distributions. P…