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

Non-line-of-sight (NLOS) imaging is attractive for its potential applications in autonomous vehicles, robotic vision, and biomedical imaging. NLOS imaging can be realized through reconstruction or recognition. Recognition is preferred in some practical scenarios because it can classify hidden objects directly and quickly. Current NLOS recognition is mostly realized by exploiting active laser illumination. However, passive NLOS recognition, which is essential for its simplified hardware system and good stealthiness, has not been explored. Here, we use a passive imaging setting that consists of a standard digital camera and an occluder to achieve a NLOS recognition system by deep learning. The proposed passive NLOS recognition system demonstrates high accuracy with the datasets of handwritten digits, hand gestures, human postures, and fashion products (81.58 % to 98.26%) using less than 1 second per image in a dark room. Beyond, good performance can be maintained under more complex lighting conditions and practical tests. Moreover, we conversely conduct white-box attacks on the NLOS recognition algorithm to study its security. An attack success rate of approximately 36% is achieved at a relatively low cost, which demonstrates that the existing passive NLOS recognition remains somewhat vulnerable to small perturbations. 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.