6 papers
RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection
Wenbin Shen, Guoxuan Qin, Guangxu Yao +4
Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level retrieval and are therefore pro…
Rethinking Multimodal Fake News Detection in the Generative AI Era
Wenbin Shen, Guoxuan Qin, Guangxu Yao +3
Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipulated material into complex fo…
MixFake: Benchmarking and Enhancing Audio Deepfake Detection in Diverse Real-world Mixed Audio
Qingcao Li, Yipeng Lin, Weichen Lian +3
Speech deepfake detection has achieved remarkable success in clean environments but faces significant challenges in complex, real-world scenarios where speech is often mixed with b…
Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability
Zijian Ying, Qianmu Li, Tao Wang +3
Various methods try to enhance adversarial transferability by improving the generalization from different perspectives. In this paper, we rethink the optimization process and propo…
Boost Adversarial Transferability by Uniform Scale and Mix Mask Method
Tao Wang, Zijian Ying, Qianmu Li +1
Adversarial examples generated from surrogate models often possess the ability to deceive other black-box models, a property known as transferability. Recent research has focused o…
Understanding CNNs from excitations
Zijian Ying, Qianmu Li, Zhichao Lian +3
Saliency maps have proven to be a highly efficacious approach for explicating the decisions of Convolutional Neural Networks. However, extant methodologies predominantly rely on gr…