5 citations · 5 across the 5 of their papers we have counts for
5 papers
Unveiling Covert Toxicity in Multimodal Data via Toxicity Association Graphs: A Graph-Based Metric and Interpretable Detection Framework
Guanzong Wu, Zihao Zhu, Siwei Lyu +1
Detecting toxicity in multimodal data remains a significant challenge, as harmful meanings often lurk beneath seemingly benign individual modalities: only emerging when modalities…
DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection
Kangran Zhao, Yupeng Chen, Xiaoyu Zhang +8
The misuse of advanced generative AI models has resulted in the widespread proliferation of falsified data, particularly forged human-centric audiovisual content, which poses subst…
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection
Delong Zhu, Yuezun Li, Baoyuan Wu +3
Face-swapping DeepFakes have become an escalating societal concern, attracting increasing attention in recent years. To counter this, we investigate a new proactive defense framewo…
Texture, Shape, Order, and Relation Matter: A New Transformer Design for Sequential DeepFake Detection
Yunfei Li, Yuezun Li, Baoyuan Wu +3
Sequential DeepFake detection is an emerging task that predicts the manipulation sequence in order. Existing methods typically formulate it as an image-to-sequence problem, employi…
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
Hanzhe Li, Jiaran Zhou, Yuezun Li +3
Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these face…