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.

非视距（NLOS）成像因其在自动驾驶车辆，机器人视觉和生物医学成像中的潜在应用而具有吸引力。NLOS成像可以通过重建或识别来实现。在某些实际情况下，识别是首选，因为它可以直接，快速地对隐藏的对象进行分类。当前的NLOS识别主要是通过利用有源激光照明来实现的。但是，尚未探索被动NLOS识别，这对于简化硬件系统和良好的隐身性至关重要。在这里，我们使用由标准数码相机和遮光器组成的被动成像设置来通过深度学习实现NLOS识别系统。拟议的被动式NLOS识别系统通过手写数字，手势，人体姿势，和时尚产品（81.58％到98.26％）在暗室中每个图像的使用时间少于1秒。除此之外，在更复杂的照明条件和实际测试下也可以保持良好的性能。此外，我们反过来对NLOS识别算法进行白盒攻击以研究其安全性。以相对较低的成本获得了大约36％的攻击成功率，这表明现有的被动NLOS识别仍然在某种程度上容易受到小扰动的影响。