Accurate but fragile passive non-line-of-sight recognition

Non-line-of-sight imaging allows reconstruction and recognition of an obscured object, but external manipulation of the data can lead to inaccurate results. Here, the accuracy and robustness of passive non-line-of-sight recognition and its fragility to adversarial attacks are studied.