A Quantum-GenAI-Enhanced Framework for Predictive and Privacy-Preserving Surveillance in CCTV Networks

The increasing reliance on closed-circuit television (CCTV) for urban security and smart infrastructure management has amplified the need for real-time surveillance systems that protect individual privacy while ensuring predictive threat intelligence. Traditional anonymization techniques compromise either identity protection or analytical utility, and recent deepfake-based adversarial attacks have further challenged the reliability of CCTV data. In this paper, we propose Q-DeSecureLink, a novel framework that combines real-time face de-identification with quantum-enhanced generative AI for adversarial data filtering, threat forecasting, and identity linkage. Q-DeSecureLink integrates privacy-preserving video transformation (via an extended DeIDLink++ module) with quantum adversarial filtering using QGANs, predictive modeling using Quantum Long Short-Term Memory (QLSTM), and explainability through Quantum Bayesian Networks (QBN). Experimental results using simulated surveillance scenarios demonstrate superior performance in identity unlinkability, adversarial resilience, and threat prediction accuracy, while maintaining real-time processing speeds. The proposed framework establishes a new direction in ethically responsible, predictive, and secure surveillance systems.