Mathematical Methods Applied in Explainable Fake Multimedia Detection
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 12753
Special Issue Editors
Interests: biological data privacy protection; multimedia content security; image recognition
Interests: image and video processing; multimedia security
Special Issue Information
Dear Colleagues,
With the tremendous progress made in computer vision and deep learning, it has become easy to generate fake multimedia data that is indistinguishable from real data. The spread of fake multimedia data could mislead the public, which may result in unforeseeable consequences. Researchers have made efforts to develop methods for the detection of fake multimedia data, most of which focus on designing deep neural networks (DNNs) against specific fake multimedia generation approaches. More efforts need to be devoted to explain why DNN models are effective and how we could design explainable approaches for robust fake multimedia detection. This Special Issue aims to promote research on both fake multimedia generation and detection techniques, including effective fake multimedia generation, explainable fake image detection, explainable fake video detection, and explainable fake audio detection. Researchers and engineers working in the field are invited to contribute original research articles that present their work. All submitted papers will be peer-reviewed and selected on the basis of both their quality and relevance to the theme of this Special Issue.
Dr. Sheng Li
Prof. Dr. Zhenjun Tang
Prof. Dr. Guorui Feng
Guest Editors
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Keywords
- deep learning
- machine learning
- multimedia security
- fake multimedia detection
- multimedia forensics
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