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Multimedia Security and Privacy
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Multimedia Security and Privacy

A special issue of Journal of Cybersecurity and Privacy (ISSN 2624-800X). This special issue belongs to the section "Security Engineering & Applications".

Deadline for manuscript submissions: closed (10 October 2025) | Viewed by 25791

Special Issue Editor

Dr. Arslan Munir

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: artificial intelligence; computer vision; parallel computing; embedded systems; secure and trustworthy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multimedia communications play a significant role in various areas of present-day, society including politics, economics, defense, healthcare, business, and entertainment. The five core building blocks of multimedia are text, image, audio, video, and animation. Multimedia, however, is susceptible to security and privacy attacks. The communication of multimedia over wired and/or wireless channels, including the Internet, exacerbates the security and privacy challenges of multimedia. Therefore, it is imperative to secure multimedia data by providing confidentiality, integrity, identity or ownership, and privacy.

This Special Issue targets security and privacy topics in multimedia. This Special Issue invites original research articles and reviews that relate to security and privacy attacks on multimedia, including artificial intelligence (AI)-based attacks, encryption techniques for multimedia, steganography, digital watermarking, digital rights management, multimedia authentication, and AI-based attack mitigation techniques. Topics of interest include, but are not limited to, the following:

  • Multimedia encryption;
  • Homomorphic encryption;
  • Multimedia authentication;
  • Steganography;
  • Steganalysis;
  • Digital watermarking;
  • Digital rights management;
  • Multimedia forensics;
  • Biometric security;
  • Quantum-based multimedia cryptosystems;
  • AI-based multimedia security;
  • Fake multimedia generation and detection;
  • Adversarial attacks and defenses;
  • AI-based deepfake videos generation and detection;
  • Multimedia privacy

Dr. Arslan Munir
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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Cybersecurity and Privacy 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 1200 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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20 pages, 953 KB  
Article
Digital Resilience and the “Awareness Gap”: An Empirical Study of Youth Perceptions of Hate Speech Governance on Meta Platforms in Hungary
by Roland Kelemen, Dorina Bosits and Zsófia Réti
J. Cybersecur. Priv. 2026, 6(1), 3; https://doi.org/10.3390/jcp6010003 - 24 Dec 2025
Viewed by 371
Abstract
Online hate speech poses a growing socio-technological threat that undermines democratic resilience and obstructs progress toward Sustainable Development Goal 16 (SDG 16). This study examines the regulatory and behavioral dimensions of this phenomenon through a combined legal analysis of platform governance and an [...] Read more.
Online hate speech poses a growing socio-technological threat that undermines democratic resilience and obstructs progress toward Sustainable Development Goal 16 (SDG 16). This study examines the regulatory and behavioral dimensions of this phenomenon through a combined legal analysis of platform governance and an empirical survey conducted on Meta platforms, based on a sample of young Hungarians (N = 301, aged 14–34). This study focuses on Hungary as a relevant case study of a Central and Eastern European (CEE) state. Countries in this region, due to their shared historical development, face similar societal challenges that are also reflected in the online sphere. The combination of high social media penetration, a highly polarized political discourse, and the tensions between platform governance and EU law (the DSA) makes the Hungarian context particularly suitable for examining digital resilience and the legal awareness of young users. The results reveal a significant “awareness gap”: While a majority of young users can intuitively identify overt hate speech, their formal understanding of platform rules is minimal. Furthermore, their sanctioning preferences often diverge from Meta’s actual policies, indicating a lack of clarity and predictability in platform governance. This gap signals a structural weakness that erodes user trust. The legal analysis highlights the limited enforceability and opacity of content moderation mechanisms, even under the Digital Services Act (DSA) framework. The empirical findings show that current self-regulation models fail to empower users with the necessary knowledge. The contribution of this study is to empirically identify and critically reframe this ‘awareness gap’. Moving beyond a simple knowledge deficit, we argue that the gap is a symptom of a deeper legitimacy crisis in platform governance. It reflects a rational user response—manifesting as digital resignation—to opaque, commercially driven, and unaccountable moderation systems. By integrating legal and behavioral insights with critical platform studies, this paper argues that achieving SDG 16 requires a dual strategy: (1) fundamentally increasing transparency and accountability in content governance to rebuild user trust, and (2) enhancing user-centered digital and legal literacy through a shared responsibility model. Such a strategy must involve both public and private actors in a coordinated, rights-based approach. Ultimately, this study calls for policy frameworks that strengthen democratic resilience not only through better regulation, but by empowering citizens to become active participants—rather than passive subjects—in the governance of online spaces. Full article
(This article belongs to the Special Issue Multimedia Security and Privacy)
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20 pages, 724 KB  
Article
A Lightweight Multimodal Framework for Misleading News Classification Using Linguistic and Behavioral Biometrics
by Mahmudul Haque, A. S. M. Hossain Bari and Marina L. Gavrilova
J. Cybersecur. Priv. 2025, 5(4), 104; https://doi.org/10.3390/jcp5040104 - 25 Nov 2025
Viewed by 523
Abstract
The widespread dissemination of misleading news presents serious challenges to public discourse, democratic institutions, and societal trust. Misleading-news classification (MNC) has been extensively studied through deep neural models that rely mainly on semantic understanding or large-scale pretrained language models. However, these methods often [...] Read more.
The widespread dissemination of misleading news presents serious challenges to public discourse, democratic institutions, and societal trust. Misleading-news classification (MNC) has been extensively studied through deep neural models that rely mainly on semantic understanding or large-scale pretrained language models. However, these methods often lack interpretability and are computationally expensive, limiting their practical use in real-time or resource-constrained environments. Existing approaches can be broadly categorized into transformer-based text encoders, hybrid CNN–LSTM frameworks, and fuzzy-logic fusion networks. To advance research on MNC, this study presents a lightweight multimodal framework that extends the Fuzzy Deep Hybrid Network (FDHN) paradigm by introducing a linguistic and behavioral biometric perspective to MNC. We reinterpret the FDHN architecture to incorporate linguistic cues such as lexical diversity, subjectivity, and contradiction scores as behavioral signatures of deception. These features are processed and fused with semantic embeddings, resulting in a model that captures both what is written and how it is written. The design of the proposed method ensures the trade-off between feature complexity and model generalizability. Experimental results demonstrate that the inclusion of lightweight linguistic and behavioral biometric features significantly enhance model performance, yielding a test accuracy of 71.91 ± 0.23% and a macro F1 score of 71.17 ± 0.26%, outperforming the state-of-the-art method. The findings of the study underscore the utility of stylistic and affective cues in MNC while highlighting the need for model simplicity to maintain robustness and adaptability. Full article
(This article belongs to the Special Issue Multimedia Security and Privacy)
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19 pages, 443 KB  
Article
Frame-Wise Steganalysis Based on Mask-Gating Attention and Deep Residual Bilinear Interaction Mechanisms for Low-Bit-Rate Speech Streams
by Congcong Sun, Azizol Abdullah, Normalia Samian and Nuur Alifah Roslan
J. Cybersecur. Priv. 2025, 5(3), 54; https://doi.org/10.3390/jcp5030054 - 4 Aug 2025
Viewed by 812
Abstract
Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalysis methods cannot extract fine-grained steganalysis features. Therefore, in this [...] Read more.
Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalysis methods cannot extract fine-grained steganalysis features. Therefore, in this paper, we propose a frame-wise steganalysis method based on mask-gating attention and bilinear codeword feature interaction mechanisms. First, this paper utilizes the mask-gating attention mechanism to dynamically learn the importance of the codewords. Second, the bilinear codeword feature interaction mechanism is used to capture an informative second-order codeword feature interaction pattern in a fine-grained way. Finally, multiple fully connected layers with a residual structure are utilized to capture higher-order codeword interaction features while preserving lower-order interaction features. The experimental results show that the performance of our method is better than that of the state-of-the-art frame-wise steganalysis method on large steganography datasets. The detection accuracy of our method is 74.46% on 1000K testing samples, whereas the detection accuracy of the state-of-the-art method is 72.32%. Full article
(This article belongs to the Special Issue Multimedia Security and Privacy)
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22 pages, 2821 KB  
Article
Blockchain-Based Evidence Trustworthiness System in Certification
by Cristina Regueiro and Borja Urquizu
J. Cybersecur. Priv. 2025, 5(1), 1; https://doi.org/10.3390/jcp5010001 - 30 Dec 2024
Cited by 1 | Viewed by 4811
Abstract
Digital evidence is a critical component in today’s organizations, as it is the foundation on which any certification is based. This paper presents a risk assessment of evidence in the certification domain to identify the main security risks. To mitigate these risks, it [...] Read more.
Digital evidence is a critical component in today’s organizations, as it is the foundation on which any certification is based. This paper presents a risk assessment of evidence in the certification domain to identify the main security risks. To mitigate these risks, it also proposes an adaptation of an existing Blockchain-based audit trail system to create an evidence trustworthiness system enhancing security and usability. This system covers specific additional requirements from auditors: evidence confidentiality and integrity verification automation. The system has been validated with cloud service providers to increase the security of evidence for a cybersecurity certification process. However, it can be also extended to other certification domains. Full article
(This article belongs to the Special Issue Multimedia Security and Privacy)
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Review

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27 pages, 1397 KB  
Review
Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
by Yousef Alghamdi and Arslan Munir
J. Cybersecur. Priv. 2024, 4(1), 126-152; https://doi.org/10.3390/jcp4010007 - 23 Feb 2024
Cited by 52 | Viewed by 16952
Abstract
Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow [...] Read more.
Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications. Full article
(This article belongs to the Special Issue Multimedia Security and Privacy)
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