Secure and Explainable AI: Enhancing Trust, Resilience, and Efficiency in Machine Learning Models
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 15 October 2025 | Viewed by 108
Special Issue Editor
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) is becoming increasingly integrated into critical domains such as healthcare, finance, autonomous systems, and smart cities. While AI-driven solutions have demonstrated remarkable performance, their security vulnerabilities and lack of explainability raise significant concerns. Adversarial attacks, data poisoning, model inversion, and other cybersecurity threats can compromise AI reliability, leading to serious real-world consequences. At the same time, the black-box nature of many machine learning models limits trust and transparency, hindering their widespread adoption.
This Special Issue focuses on advancing secure and explainable AI (XAI) by addressing key challenges in adversarial robustness, AI security, and computationally efficient explainability. We invite original research papers, surveys, and case studies that explore novel methodologies, algorithms, and practical implementations to enhance the trustworthiness and resilience of AI systems.
Topics of interest include, but are not limited to, the following:
- Adversarial robustness in AI: Detection and defense strategies against adversarial attacks (e.g., FGSM, PGD, DeepFool, and TextAttack).
- Explainable AI (XAI) for security: Interpretable models using SHAP, LIME, saliency maps, and decision tree surrogates for security applications.
- Lightweight and efficient AI security: Optimized security techniques for reducing computational overhead while maintaining robustness.
- Privacy-preserving AI security: Federated learning, homomorphic encryption, differential privacy, and secure multiparty computation for AI models.
- Trustworthy AI in smart cities and healthcare: Securing AI-driven traffic monitoring, surveillance, and medical diagnostics against adversarial threats.
- Hybrid defense mechanisms: Combining traditional cybersecurity techniques with AI-based threat detection for robust defenses.
- Real-world case studies and applications: Practical implementations of AI security and XAI in various domains.
By bringing together contributions from researchers and practitioners, this Special Issue aims to bridge the gap between AI security and explainability, fostering the development of trustworthy AI solutions for real-world applications.
We look forward to receiving your contributions.
Dr. Sarfraz Brohi
Guest Editor
Manuscript Submission Information
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Keywords
- AI security
- explainable AI (XAI)
- adversarial attacks and defenses
- robust machine learning
- AI for healthcare
- AI for smart cities
- privacy-preserving AI
- computational efficiency in AI security
- trustworthy AI
- AI for cybersecurity
- AI model interpretability
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