Appl Opt. 2021 Feb 20;60(6):1686-1693. doi: 10.1364/AO.415120.
We proposed a novel cryptographic imaging scheme that is the combination of optical encryption and computational decryption. To prevent personal privacy from being spied upon amid the imaging formation process, in this study we applied a coded mask to optically encrypt the scene and utilized the deep neural network for computational decryption. For encryption, the sensor recorded a new representation of the original signal, not being distinguishable by humans on purpose. For decryption, we successfully reconstructed the image with the mean squared error equal to 0.028, and 100% for the classification through the Japanese Female Facial Expression dataset. By means of the feature visualization, we found that the coded mask served as a linear operator to synthesize the spatial fidelity of the original scene, but kept the features for the post-recognition process. We believe the proposed framework can inspire more possibilities for the unconventional imaging system.
我们提出了一种新颖的加密成像方案，它结合了光学加密和计算解密。为了防止在成像形成过程中个人隐私被窥探，在本研究中，我们应用编码掩模对场景进行光学加密，并利用深度神经网络进行计算解密。对于加密，传感器记录原始信号的新表示形式，故意使人眼无法分辨。对于解密，我们成功地通过日本女性面部表情数据集以均方误差等于 0.028 进行图像重建，分类准确率为 100%。通过特征可视化，我们发现编码掩模起到了线性算子的作用，合成了原始场景的空间保真度，但保留了用于后续识别过程的特征。我们相信，所提出的框架可以为非常规成像系统激发更多的可能性。
