Advanced Deepfake Detection & AI-Driven Intrusion Detection for Secure Digital Ecosystems

Our research team brings strong interdisciplinary expertise in deepfake detection, multimedia forgery analysis, and AI-based intrusion detection systems designed for IoT and IoMT ecosystems. For more than a decade, we have developed robust methods for detecting manipulated audio, image, and video content, including deepfake voices, GAN-generated media, and adversarially crafted attacks targeting critical infrastructures. Our work integrates state-of-the-art machine learning, explainable AI (XAI), secure and privacy-preserving federated learning, and anomaly-based IDS architectures capable of identifying sophisticated cyber-threats in distributed environments. We have extensive experience in academic research, European collaboration, and national R&D projects, and we actively contribute to emerging cybersecurity and AI standards. Within Horizon collaborations, we aim to support project consortia with advanced algorithms, secure model training strategies, data integrity mechanisms, and deepfake-resistant AI pipelines that enhance trust, resilience, and robustness across digital systems. We are fully prepared to act as a technical partner, contributing both research excellence and hands-on implementation capacity to strengthen the security and reliability of next-generation AI-powered platforms.