1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
Does Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?
Yuankun Xie, Chenxu Xiong, Xiaopeng Wang +9
Currently, Audio Language Models (ALMs) are rapidly advancing due to the developments in large language models and audio neural codecs. These ALMs have significantly lowered the ba…
A Noval Feature via Color Quantisation for Fake Audio Detection
Zhiyong Wang, Xiaopeng Wang, Yuankun Xie +9
In the field of deepfake detection, previous studies focus on using reconstruction or mask and prediction methods to train pre-trained models, which are then transferred to fake au…
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…
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…
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio
Yi Lu, Yuankun Xie, Ruibo Fu +9
With the proliferation of Large Language Model (LLM) based deepfake audio, there is an urgent need for effective detection methods. Previous deepfake audio generation methods typic…