Hidden Camera Detection in Smart Environments: A Comprehensive Survey of Current Methods, Challenges, and Future Perspectives

The rapid proliferation of functional network devices equipped with imaging capabilities across consumer, industrial, security, and defense sectors has enhanced convenience and safety. However, this widespread adoption has simultaneously raised critical concerns regarding covert surveillance and privacy violations. In recent years, research on detecting and localizing hidden spy cameras has made substantial progress, driven by the urgent need for reliable and robust counter-surveillance technologies. While hidden camera detection has emerged as a critical research area, existing efforts remain dispersed across various domains without a unifying perspective. By systematically reviewing 61 core studies published since 2021, this survey consolidates existing approaches into four paradigms and provides a structured foundation for comparative analysis. The reviewed techniques are systematically categorized into three primary sensing domains: network-based, optical, and spectrum-oriented approaches. The fourth paradigm encompasses hybrid architectures that integrate machine learning to enhance detection accuracy. This survey provides a comprehensive taxonomic framework that unifies detection and localization approaches for hidden camera countermeasures. By systematically reviewing current technologies and identifying research gaps, we aim to provide strategic guidance on privacy-preserving detection systems that can effectively counter evolving surveillance threats. Beyond taxonomic synthesis, this survey provides a strategic roadmap for designing reproducible evaluations and selecting optimal detection pipelines under real-world constraints.