6 papers
Empowering Long-form Omni-modal Understanding with Robust Audio Perception
Kaiying Yan, Luoyi Sun, Xiao Zhou +1
Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, lar…
Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning
Kaiying Yan, Moyang Liu, Yukun Liu +4
The rapid spread of fake news across multimedia platforms presents serious challenges to information credibility. In this paper, we propose a Debunk-and-Infer framework for Fake Ne…
Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention
Moyang Liu, Kaiying Yan, Yukun Liu +4
The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Traditional unimodal…
Exploring Modality Disruption in Multimodal Fake News Detection
Moyang Liu, Kaiying Yan, Yukun Liu +4
The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Compared to unimodal…
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Yiming Li, Kaiying Yan, Shuo Shao +4
With the increasing adoption of deep learning in speaker verification, large-scale speech datasets have become valuable intellectual property. To audit and prevent the unauthorized…
MTPareto: A MultiModal Targeted Pareto Framework for Fake News Detection
Kaiying Yan, Moyang Liu, Yukun Liu +5
Multimodal fake news detection is essential for maintaining the authenticity of Internet multimedia information. Significant differences in form and content of multimodal informati…