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Cutting-Edge Advances in Intelligent Media: Detection, Analysis, Generation, and Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 622

Special Issue Editor


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Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Interests: natural language processing; network content security; large language model applications

Special Issue Information

Dear Colleagues,

The rapid development of generative AI has catalyzed a paradigm shift in social media, marked by a transition from user-generated content (UGC)- to AI-generated content (AIGC)-centered intelligent media platforms. While AIGC has introduced transformative innovations such as AI-assisted writing and content creation, it has simultaneously raised critical concerns regarding AI-driven information pollution and content authenticity. In this context, the development of robust mechanisms for intelligent media content detection, analysis, and generation has become paramount in the AIGC era, necessitating comprehensive research and innovative solutions to harness the potential of AI while mitigating its risks.

We invite you to contribute a peer-reviewed, comprehensive review or original research paper for possible publication in this Special Issue. 

Dr. Lianwei Wu
Guest Editor

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.

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Keywords

  • fake news detection
  • AI-generated content detection
  • detection of deepfakes
  • analysis of AI-generated content dissemination in social media
  • research on persuasion strategies based on large models
  • opinion mining based on large models
  • multimodal content analysis based on large models
  • multimodal content generation based on large models

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Published Papers (1 paper)

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Research

21 pages, 6048 KiB  
Article
GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
by Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar and Glenn Van Wallendael
Appl. Sci. 2025, 15(12), 6622; https://doi.org/10.3390/app15126622 - 12 Jun 2025
Viewed by 509
Abstract
Deepfakes have raised significant concerns due to their potential to spread false information and compromise the integrity of digital media. Current deepfake detection models often struggle to generalize across a diverse range of deepfake generation techniques and video content. In this work, we [...] Read more.
Deepfakes have raised significant concerns due to their potential to spread false information and compromise the integrity of digital media. Current deepfake detection models often struggle to generalize across a diverse range of deepfake generation techniques and video content. In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes an Autoencoder and Variational Autoencoder to learn from latent data distributions. By learning from the visual artifacts and latent data distribution, GenConViT achieves an improved performance in detecting a wide range of deepfake videos. The model is trained and evaluated on DFDC, FF++, TM, DeepfakeTIMIT, and Celeb-DF (v2) datasets. The proposed GenConViT model demonstrates strong performance in deepfake video detection, achieving high accuracy across the tested datasets. While our model shows promising results in deepfake video detection by leveraging visual and latent features, we demonstrate that further work is needed to improve its generalizability when encountering out-of-distribution data. Our model provides an effective solution for identifying a wide range of fake videos while preserving the integrity of media. Full article
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