Unlawful video surveillance of unsuspecting individuals using spy cameras has become an increasing concern. To mitigate these threats, there are both commercial products and research prototypes designed to detect hidden spy cameras in household and office environments. However, existing work often relies heavily on user expertise and only applies to wireless cameras. To bridge this gap, we propose HeatDeCam, a thermal-imagery-based spy camera detector, capable of detecting hidden spy cameras with or without built-in wireless connectivity. To reduce the reliance on user expertise, HeatDeCam leverages a compact neural network deployed on a smartphone to recognize unique heat dissipation patterns of spy cameras. To evaluate the proposed system, we have collected and open-sourced a dataset of a total of 22506 thermal and visual images. These images consist of 11 spy cameras collected from 6 rooms across different environmental conditions. Using this dataset, we found HeatDeCam can achieve over 95% accuracy in detecting hidden cameras. We have also conducted a usability evaluation involving a total of 416 participants using both an online survey and an in-person usability test to validate HeatDeCam.