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Mathematics
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Mathematical Methods Applied in Explainable Fake Multimedia Detection
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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: 31 August 2024 | Viewed by 7474

Special Issue Editors

Dr. Sheng Li

E-Mail Website
Guest Editor
Shanghai Institute of Intelligent Electronics and Systems, School of Computer Science, Fudan University, Shanghai 201203, China
Interests: biological data privacy protection; multimedia content security; image recognition
Prof. Dr. Zhenjun Tang

E-Mail Website
Guest Editor
Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China
Interests: image and video processing; multimedia security
Prof. Dr. Guorui Feng

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • machine learning
  • multimedia security
  • fake multimedia detection
  • multimedia forensics

Published Papers (5 papers)

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Research

15 pages, 4127 KiB  
Article
A Moiré Removal Method Based on Peak Filtering and Image Enhancement
by Wenfa Qi, Xinquan Yu, Xiaolong Li and Shuangyong Kang
Mathematics 2024, 12(6), 846; https://doi.org/10.3390/math12060846 - 14 Mar 2024
Viewed by 454
Abstract
Screen photos often suffer from moiré patterns, which significantly affect their visual quality. Although many deep learning-based methods for removing moiré patterns have been proposed, they fail to recover images with complex textures and heavy moiré patterns. Here, we focus on text images [...] Read more.
Screen photos often suffer from moiré patterns, which significantly affect their visual quality. Although many deep learning-based methods for removing moiré patterns have been proposed, they fail to recover images with complex textures and heavy moiré patterns. Here, we focus on text images with heavy moiré patterns and propose a new demoiré approach, incorporating frequency-domain peak filtering and spatial-domain visual quality enhancement. We find that the content of the text image mainly lies in the central region, whereas the moiré pattern lies in the peak region, in the frequency domain. Based on this observation, a peak-filtering algorithm and a central region recovery strategy are proposed to accurately locate and remove moiré patterns while preserving the text parts. In addition, to further remove the noisy background and paint the missing text parts, an image enhancement algorithm utilising the Otsu method is developed. Extensive experimental results show that the proposed method significantly removes severe moiré patterns from images with better visual quality and lower time cost compared to the state-of-the-art methods. Full article
(This article belongs to the Special Issue Mathematical Methods Applied in Explainable Fake Multimedia Detection)
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11 pages, 830 KiB  
Article
High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images
by Xiang Li, Xiaolong Li, Mengyao Xiao, Yao Zhao and Hsunfang Cho
Mathematics 2023, 11(19), 4111; https://doi.org/10.3390/math11194111 - 28 Sep 2023
Cited by 1 | Viewed by 677
Abstract
Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding [...] Read more.
Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding data into visually insensitive regions (e.g., edges and textures). In this paper, following this direction, a high-capacity RDH approach based on multi-embedding is proposed. The cover image is decoupled to select the embedding sequence that can better utilize texture pixels and reduce the size of the reconstruction information, and a multi-embedding strategy is proposed to embed the secret data along with the reconstruction information by matrix embedding. The experimental results demonstrate that the proposed method provides a superior visual quality and higher embedding capacity than some state-of-the-art RDH works for binary images. With an embedding capacity of 1000 bits, the proposed method achieves an average PSNR of 49.45 dB and an average SSIM of 0.9705 on the test images. This marks an improvement of 1.1 dB in PSNR and 0.0242 in SSIM compared to the latest state-of-the-art RDH method. Full article
(This article belongs to the Special Issue Mathematical Methods Applied in Explainable Fake Multimedia Detection)
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13 pages, 7077 KiB  
Article
Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network
by Muhammad Asad Arshed, Ayed Alwadain, Rao Faizan Ali, Shahzad Mumtaz, Muhammad Ibrahim and Amgad Muneer
Mathematics 2023, 11(17), 3710; https://doi.org/10.3390/math11173710 - 29 Aug 2023
Cited by 3 | Viewed by 1805
Abstract
With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with [...] Read more.
With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the Vision Transformer (ViT), i.e., extracting global features to detect deepfake images effectively. After conducting comprehensive experiments, our method demonstrates a high level of effectiveness, achieving a detection accuracy, precision, recall, and F1 rate of 99.5 to 100% for both the original and mixture data set. According to our existing understanding, this study is a research endeavor incorporating real-world applications, specifically examining Snapchat-filtered images. Full article
(This article belongs to the Special Issue Mathematical Methods Applied in Explainable Fake Multimedia Detection)
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16 pages, 443 KiB  
Article
Achieving a Secure and Traceable High-Definition Multimedia Data Trading Scheme Based on Blockchain
by Shuguang Zhao, Zhihua Zeng, Jiahui Peng and Feng Yu
Mathematics 2023, 11(10), 2224; https://doi.org/10.3390/math11102224 - 09 May 2023
Cited by 2 | Viewed by 1270
Abstract
The Internet has penetrated into every aspect of life. Large amounts of data are generated by multimedia collection equipment every day. As an asset, data can achieve value circulation through transactions. However, the existing centralized transaction model is not secure enough, has the [...] Read more.
The Internet has penetrated into every aspect of life. Large amounts of data are generated by multimedia collection equipment every day. As an asset, data can achieve value circulation through transactions. However, the existing centralized transaction model is not secure enough, has the risk of user privacy leakage, and the protection of data copyright is insufficient. In this paper, in order to solve the transaction security and traceability problems of multimedia data, especially high-definition data such as vector graphics, we implement a transaction scheme STTS without third-party based on blockchain. For high-definition multimedia data, we use zero watermarking combined with oblivious transfer to embed copyright information. A two-stage verification process is then implemented by using group signature and a secret sharing scheme to complete data distribution. Finally, the smart contract is used to complete copyright tracking. We test the performance of our scheme by simulating the real transaction environment in the Internet of Things (IoT) and demonstrate the feasibility of our scheme, which can be applied to large-scale multimedia data trading schemes in the IoT. Full article
(This article belongs to the Special Issue Mathematical Methods Applied in Explainable Fake Multimedia Detection)
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13 pages, 2238 KiB  
Article
Frequency Domain Filtered Residual Network for Deepfake Detection
by Bo Wang, Xiaohan Wu, Yeling Tang, Yanyan Ma, Zihao Shan and Fei Wei
Mathematics 2023, 11(4), 816; https://doi.org/10.3390/math11040816 - 06 Feb 2023
Cited by 7 | Viewed by 2297
Abstract
As deepfake becomes more sophisticated, the demand for fake facial image detection is increasing. Although great progress has been made in deepfake detection, the performance of most existing deepfake detection methods degrade significantly when these methods are applied to detect low-quality images for [...] Read more.
As deepfake becomes more sophisticated, the demand for fake facial image detection is increasing. Although great progress has been made in deepfake detection, the performance of most existing deepfake detection methods degrade significantly when these methods are applied to detect low-quality images for the disappearance of key clues during the compression process. In this work, we mine frequency domain and RGB domain information to specifically improve the detection of low-quality compressed deepfake images. Our method consists of two modules: (1) a preprocessing module and (2) a classification module. In the preprocessing module, we utilize the Haar wavelet transform and residual calculation to obtain the mid-high frequency joint information and fuse the frequency map with the RGB input. In the classification module, the image obtained by concatenation is fed to the convolutional neural network for classification. Because of the combination of RGB and frequency domain, the robustness of the model has been greatly improved. Our extensive experimental results demonstrate that our approach can not only achieve excellent performance when detecting low-quality compressed deepfake images, but also maintain great performance with high-quality images. Full article
(This article belongs to the Special Issue Mathematical Methods Applied in Explainable Fake Multimedia Detection)
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