2 citations · 3 across the 3 of their papers we have counts for
5 papers
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…
From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes
Long Ma, Zhiyuan Yan, Jin Xu +5
Detecting deepfakes has been an increasingly important topic, especially given the rapid development of AI generation techniques. In this paper, we ask: How can we build a universa…
Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
Siwei Wen, Junyan Ye, Peilin Feng +7
With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challe…
X2-DFD: A framework for eXplainable and eXtendable Deepfake Detection
Yize Chen, Zhiyuan Yan, Guangliang Cheng +3
This paper proposes X2-DFD, an eXplainable and eXtendable framework based on multimodal large-language models (MLLMs) for deepfake detection, consisting of three key stages. The fi…
Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
Shan Jia, Reilin Lyu, Kangran Zhao +7
DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation. Detecting DeepFakes is currently solved with…