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Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and...
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Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and Privacy

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1847

Special Issue Editors

Dr. Ziyang He

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: deep learning; federated learning; IoT security; privacy protection
Prof. Dr. Yangjie Cao

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: AI security; machine intelligence and human–computer interaction; intelligent big data processing; cloud computing and high-performance computing
Special Issues, Collections and Topics in MDPI journals
Dr. Minglin Liu

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: information hiding; AI security; steganography; watermarking

Special Issue Information

Dear Colleagues,

The rapid advancement and widespread adoption of Artificial Intelligence (AI) have profoundly transformed application development across various domains. While offering unprecedented capabilities, this integration has introduced significant challenges in ensuring security and privacy. AI systems themselves can be vulnerable to attacks such as data poisoning, adversarial examples, and model inversion, while their deployment raises critical concerns regarding data privacy, algorithmic bias, and accountability. Building AI that is not only intelligent but also trustworthy—robust, secure, fair, and transparent—has, therefore, become an imperative for achieving sustainable and safe technological progress.

This Special Issue will compile cutting-edge research and innovative solutions dedicated to the development and implementation of trustworthy AI with a specific focus on application security and privacy. We seek contributions that address the fundamental principles, architectural designs, and practical implementations of AI systems that can defend against evolving threats, protect sensitive data, and operate reliably in real-world scenarios. Our goal is to foster discussions on holistic frameworks that integrate security and privacy into the very fabric of AI models and systems.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Explainable and Transparent AI for Security Analysis;
  • Adversarial Machine Learning and Robust Defense Mechanisms;
  • Privacy-Preserving AI Techniques (e.g., Federated Learning, Differential Privacy);
  • AI-Driven Information Hiding and Steganography;
  • Secure AI Architecture and System Design;
  • AI for Threat Detection and Intrusion Prevention;
  • Trustworthy AI in Critical Applications (e.g., Healthcare, IoT);
  • AI-Powered Authentication and Access Control;
  • Security of AI Models Against Data Poisoning and Model Inversion;
  • Covert Communication and Data Protection Using Generative Models;
  • Detection of Steganography and Deepfakes;
  • Integration of Hardware Security with AI Workflows;
  • Benchmarks and Tools for Trustworthy AI Implementation.

We invite researchers, practitioners, and industry experts to submit original research articles, comprehensive reviews, and insightful case studies that contribute to the advancement of trustworthy AI in securing applications and safeguarding privacy.

Dr. Ziyang He
Dr. Yangjie Cao
Dr. Minglin Liu
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 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. Electronics 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 2400 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

  • trustworthy AI
  • AI security
  • data poisoning defense
  • membership inference attacks
  • adversarial machine learning
  • privacy-preserving
  • information hiding
  • federated learning
  • digital steganography
  • explainable AI (XAI) for security
  • AI for intrusion detection

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Published Papers (3 papers)

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Research

20 pages, 6356 KB  
Article
A Low-Complexity CW Radar for Detecting High-Precision Tiny Vibration
by Chao Wang, Yiming Wang, Xiaoyue Wei, Jinpeng Shi, Zili Jiao, Pengsong Duan and Yangjie Cao
Electronics 2026, 15(9), 1820; https://doi.org/10.3390/electronics15091820 - 24 Apr 2026
Viewed by 327
Abstract
Research on methods for detecting microwave-based noncontact vibration has garnered significant attention in recent years. To simplify system complexity and reduce costs, which would enable broader application of radar technology in daily life, we propose a low-complexity, high-precision continuous-wave (CW) radar system for [...] Read more.
Research on methods for detecting microwave-based noncontact vibration has garnered significant attention in recent years. To simplify system complexity and reduce costs, which would enable broader application of radar technology in daily life, we propose a low-complexity, high-precision continuous-wave (CW) radar system for noncontact vibration detection. This system employs a hardware-based approach for phase comparison to extract vibration information, enabling simultaneous detection of both vibration amplitude and frequency under a CW radar architecture. In this study, we establish a phase discrimination error model to characterize the inconsistent detection sensitivity of the hardware phase comparator in different phase intervals, and we further propose a phase compensation scheme to mitigate the nonlinearity of phase discrimination and the “null-point” problem in continuous phase comparison, consequently improving the sensitivity and precision of the proposed radar system. Through loudspeaker vibration and experiments on human vital signs, the system maintains a vibration amplitude detection accuracy above 90.3% within 1.8 m while achieving respiratory rate and heartbeat rate detection accuracies of 96.34% and 98.02%, respectively. Full article
(This article belongs to the Special Issue Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and Privacy)
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25 pages, 769 KB  
Article
Standard-Oriented Architecture for AI-Powered Information Security Risk Management
by Oleksii Chalyi, Kęstutis Driaunys, Šarūnas Grigaliūnas and Rasa Brūzgienė
Electronics 2026, 15(6), 1282; https://doi.org/10.3390/electronics15061282 - 19 Mar 2026
Viewed by 761
Abstract
This paper presents a standard-oriented architecture for automating information security risk management (ISRM) using artificial intelligence. The study first evaluates eight international frameworks (including COBIT 2019, NIST SP 800-53, and ISO 31000) for automation suitability, identifying ISO/IEC 27005 as the optimal structural foundation. [...] Read more.
This paper presents a standard-oriented architecture for automating information security risk management (ISRM) using artificial intelligence. The study first evaluates eight international frameworks (including COBIT 2019, NIST SP 800-53, and ISO 31000) for automation suitability, identifying ISO/IEC 27005 as the optimal structural foundation. Based on these findings, an architecture integrating Natural Language Processing and machine learning to automate risk identification, assessment, and treatment is proposed. A core component is a decision-making module that combines expert reasoning with a Multi-LLM consensus mechanism to ensure reliability. To provide exploratory support for the proposed architecture, a comparative study using five state-of-the-art Large Language Models (ChatGPT, Gemini Advanced, Grok, Microsoft Copilot, and DeepSeek Chat) was conducted on a standardized risk identification task. The results highlight strong cross-model consensus patterns, providing exploratory evidence that LLMs may support expert-informed risk identification and reasoning tasks while acknowledging the current limitations in complex reasoning. This approach proposes a transparent architectural foundation for AI-driven ISRM whose scalability must be established through future prototype-based evaluation, thereby bridging the gap between rigid compliance standards and generative AI capabilities. Full article
(This article belongs to the Special Issue Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and Privacy)
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22 pages, 2090 KB  
Article
Mini-Hide: Generative Image Steganography via Flip Watermarking for Reducing BER
by Rixuan Qiu, Zhiyuan Luo, Ruixiang Fan, Na Cao, Yuan Wang and Cong Yang
Electronics 2026, 15(5), 939; https://doi.org/10.3390/electronics15050939 - 25 Feb 2026
Viewed by 494
Abstract
Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or [...] Read more.
Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or padding to match the model input size. In addition, it is difficult to balance BER reduction and imperceptibility of stego-images. To address these issues, this paper proposes a novel generative image steganography algorithm based on flip watermarking, with the core novelty of designing a mirror flipping preprocessing mechanism to achieve a redundant watermark and eliminate information errors caused by duplication or padding, and constructing an end-to-end Mini-Hide steganographic framework to integrate flip watermarking with generative steganography for the first time. Specifically, the proposed method first converts the binary bitstream of secret information into a square matrix, and performs vertical, horizontal and vertical–horizontal mirror flipping on the matrix to form a redundant basic watermark, which is then expanded to a secret image with the same size as the cover image. After that, the secret image is preprocessed by a preparation network and then input into an encoding network together with the cover image to generate a stego-image. Finally, the generated stego-image is input into the decoding network to extract the secret image. Subsequently, the inverse operation of flip watermarking is performed on the extracted secret image to recover the original binary bitstream. Extensive experiments are conducted on the public COCO dataset (256×256 pixels) with BER, PSNR, and SSIM, and the proposed method is compared with state-of-the-art generative steganographic methods. Quantitative results show that the proposed method achieves a 0% BER for secret information of 8×8 to 64×64 bits, and the BER is only 0.00002% for 256×256-bit secret information; the PSNR of stego-images reaches 37.75 dB, and the SSIM hits 0.96, which are 7.07 dB and 0.02 higher than those of the classic HiDDeN method (64×64 bit) respectively. We also validated the flip watermark module by integrating into other methods; the results also show that the PSNR of FNNS-D is improved by 13.12 dB (256×256), and the BER of SteganoGAN is reduced by 99.99% (256×256 bit). In addition, the proposed method breaks the embedding size limit of HiDDeN (≤64×64 bit) and supports up to 256×256-bit secret information embedding with stable performance. This work significantly reduces the BER of generative image steganography while improving the visual quality of stego-images, provides a new preprocessing and optimization scheme for low-BER generative steganographic algorithm design, and also offers a universal lightweight module for performance improvement of existing steganographic methods, which has important theoretical and practical significance for enhancing the security and reliability of covert information transmission in the field of information security. Full article
(This article belongs to the Special Issue Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and Privacy)
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