Hidden spy cameras are a growing worldwide threat to people’s intimacy and privacy. With the growing interest in full-screen devices and the underlying development of under-screen cameras, a new type of potential security risk is introduced. Recent smartphones such as the ZTE AXON 40 already demonstrate that it’s infeasible to detect the camera with the human eye. There exist several techniques to detect hidden cameras, however most of these techniques are not resilient to the unique deployment scenario of the under-screen camera. A recent optical detection technique, which relies on the retro-reflective effect of hidden cameras, is promising but is also greatly hindered by challenges introduced due to reflections from the screen that is placed in front of the under-screen camera. In this work, these challenges are addressed, by proposing a detection principle that exploits the difference in reflective nature between the USC and the screen. Using reflection detection in a sliding window approach, a detection methodology is given to detect the USC. Furthermore, a detection architecture is designed that incorporates the proposed detection principles using a combination of computer vision, image processing and machine learning techniques. Using an off-the-shelf Time-of-Flight sensor, this architecture is implemented into a detection system and evaluated on its robustness and detection accuracy. Experiments on a dataset of 200 videos with a variety of measurement conditions show that this detection system is capable of achieving a USC detection rate of 71.5% while having a false-positive rate of 21.5%. It also proves excellent results while the screen is displaying content.