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Mathematics
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New Solutions for Multimedia and Artificial Intelligence Security
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New Solutions for Multimedia and Artificial Intelligence Security

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1346

Special Issue Editor

Dr. Hanzhou Wu

E-Mail Website
Guest Editor
Department of Electronic and Information Engineering, Shanghai University, Shanghai 200444, China
Interests: multimedia security; security and communication networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the recent decade, there has been an increase in the application of artificial intelligence (AI) technologies for solving the complex problems arising in multimedia security. For example, deep neural networks (DNNs) have been widely applied in steganography and digital watermarking to enhance the rate-distortion performance of multimedia communication systems. However, the fast development of AI is also accompanied by new challenges in the field of multimedia security. AI models can be applied to enhance the performance of conventional active and passive multimedia forensic systems to protect against common types of attacks. However, novel AI models may be developed by adversaries with malicious intent, such as AI models for generating multimedia forgeries and disinformation. Moreover, extensive research demonstrates that AI models are inherently vulnerable to malicious attacks, such as adversarial perturbations. In turn, this will cause AI models to easily make incorrect decisions, seriously threatening the security and reliability of multimedia and AI systems. Therefore, it is necessary to seek more reliable and robust defensive solutions for multimedia and AI security systems.

This Special Issue aims to attract the latest developments and trends in AI-based multimedia security. Moreover, we encourage the submission of high-quality original research and review papers from academia and industry.

Dr. Hanzhou Wu
Guest Editor

Manuscript Submission Information

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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

  • multimedia forensics and anti-forensics
  • adversarial attacks and defenses
  • deepfake and misinformation detection
  • steganography and steganalysis
  • multimedia watermarking and DNN watermarking
  • novel data embedding techniques in various covers
  • interpretability and theoretical analysis of forensic techniques

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Published Papers (3 papers)

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Research

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33 pages, 6362 KiB  
Article
SG-ResNet: Spatially Adaptive Gabor Residual Networks with Density-Peak Guidance for Joint Image Steganalysis and Payload Location
by Zhengliang Lai, Chenyi Wu, Xishun Zhu, Jianhua Wu and Guiqin Duan
Mathematics 2025, 13(9), 1460; https://doi.org/10.3390/math13091460 - 29 Apr 2025
Abstract
Image steganalysis detects hidden information in digital images by identifying statistical anomalies, serving as a forensic tool to reveal potential covert communication. The field of deep learning-based image steganography has relatively scarce effective steganalysis methods, particularly those designed to extract hidden information. This [...] Read more.
Image steganalysis detects hidden information in digital images by identifying statistical anomalies, serving as a forensic tool to reveal potential covert communication. The field of deep learning-based image steganography has relatively scarce effective steganalysis methods, particularly those designed to extract hidden information. This paper introduces an innovative image steganalysis method based on generative adaptive Gabor residual networks with density-peak guidance (SG-ResNet). SG-ResNet employs a dual-stream collaborative architecture to achieve precise detection and reconstruction of steganographic information. The classification subnet utilizes dual-frequency adaptive Gabor convolutional kernels to decouple high-frequency texture and low-frequency contour components in images. It combines a density peak clustering with three quantization and transformation-enhanced convolutional blocks to generate steganographic covariance matrices, enhancing the weak steganographic signals. The reconstruction subnet synchronously constructs multi-scale features, preserves steganographic spatial fingerprints with channel-separated residual spatial rich model and pixel reorganization operators, and achieves sub-pixel-level steganographic localization via iterative optimization mechanism of feedback residual modules. Experimental results obtained with datasets generated by several public steganography algorithms demonstrate that SG-ResNet achieves State-of-the-Art results in terms of detection accuracy, with 0.94, and with a PSNR of 29 between reconstructed and original secret images. Full article
(This article belongs to the Special Issue New Solutions for Multimedia and Artificial Intelligence Security)
15 pages, 1315 KiB  
Article
Leveraging Universal Adversarial Perturbation and Frequency Band Filters Against Face Recognition
by Limengnan Zhou, Bufan He, Xi Jin and Guangling Sun
Mathematics 2024, 12(20), 3287; https://doi.org/10.3390/math12203287 - 20 Oct 2024
Cited by 1 | Viewed by 847
Abstract
Universal adversarial perturbation (UAP) exhibits universality as it is independent of specific images. Although previous investigations have shown that the classification of natural images is susceptible to universal adversarial attacks, the impact of UAP on face recognition has not been fully investigated. Thus, [...] Read more.
Universal adversarial perturbation (UAP) exhibits universality as it is independent of specific images. Although previous investigations have shown that the classification of natural images is susceptible to universal adversarial attacks, the impact of UAP on face recognition has not been fully investigated. Thus, in this paper we assess the vulnerability of face recognition for UAP. We propose FaUAP-FBF, which exploits the frequency domain by learning high, middle, and low band filters as an additional dimension of refining facial UAP. The facial UAP and filters are alternately and repeatedly learned from a training set. Furthermore, we convert non-target attacks to target attacks by customizing a target example, which is an out-of-distribution sample for a training set. Accordingly, non-target and target attacks form a uniform target attack. Finally, the variance of cosine similarity is incorporated into the adversarial loss, thereby enhancing the attacking capability. Extensive experiments on LFW and CASIA-WebFace datasets show that FaUAP-FBF has a higher fooling rate and better objective stealthiness metrics across the evaluated network structures compared to existing universal adversarial attacks, which confirms the effectiveness of the proposed FaUAP-FBF. Our results also imply that UAP poses a real threat for face recognition systems and should be taken seriously when face recognition systems are being designed. Full article
(This article belongs to the Special Issue New Solutions for Multimedia and Artificial Intelligence Security)
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Review

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27 pages, 3140 KiB  
Review
Watermarking for Large Language Models: A Survey
by Zhiguang Yang, Gejian Zhao and Hanzhou Wu
Mathematics 2025, 13(9), 1420; https://doi.org/10.3390/math13091420 - 26 Apr 2025
Viewed by 286
Abstract
With the rapid advancement and widespread deployment of large language models (LLMs), concerns regarding content provenance, intellectual property protection, and security threats have become increasingly prominent. Watermarking techniques have emerged as a promising solution for embedding verifiable signals into model outputs, enabling attribution, [...] Read more.
With the rapid advancement and widespread deployment of large language models (LLMs), concerns regarding content provenance, intellectual property protection, and security threats have become increasingly prominent. Watermarking techniques have emerged as a promising solution for embedding verifiable signals into model outputs, enabling attribution, authentication, and mitigation of unauthorized usage. Despite growing interest in watermarking LLMs, the field lacks a systematic review to consolidate existing research and assess the effectiveness of different techniques. Key challenges include the absence of a unified taxonomy and limited understanding of trade-offs between capacity, robustness, and imperceptibility in real-world scenarios. This paper addresses these gaps by providing a comprehensive survey of watermarking methods tailored to LLMs, structured around three core contributions: (1) We classify these methods as training-free and training-based approaches and detail their mechanisms, strengths, and limitations to establish a structured understanding of existing techniques. (2) We evaluate these techniques based on key criteria—including robustness, imperceptibility, and payload capacity—to identify their effectiveness and limitations, highlighting challenges in designing resilient and practical watermarking solutions. (3) We also discuss critical open challenges while outlining future research directions and practical considerations to drive innovation in watermarking for LLMs. By providing a structured synthesis, this work advances the development of secure and effective watermarking solutions for LLMs. Full article
(This article belongs to the Special Issue New Solutions for Multimedia and Artificial Intelligence Security)
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