Data protection methods like cryptography, despite being effective, inadvertently signal the presence of secret communication, thereby drawing undue attention. Here, we introduce an optical information hiding camera integrated with an electronic decoder, optimized jointly through deep learning. This information hiding-decoding system employs a diffractive optical processor as its front-end, which transforms and hides input images in the form of ordinary-looking patterns that deceive/mislead human observers. This information hiding transformation is valid for infinitely many combinations of secret messages, all of which are transformed into ordinary-looking output patterns, achieved all-optically through passive light-matter interactions within the optical processor. By processing these ordinary-looking output images, a jointly-trained electronic decoder neural network accurately reconstructs the original information hidden within the deceptive output pattern. We numerically demonstrated our approach by designing an information hiding diffractive camera along with a jointly-optimized convolutional decoder neural network. The efficacy of this system was demonstrated under various lighting conditions and noise levels, showing its robustness. We further extended this information hiding camera to multi-spectral operation, allowing the concealment and decoding of multiple images at different wavelengths, all performed simultaneously in a single feed-forward operation. The feasibility of our framework was also demonstrated experimentally using THz radiation. This optical encoder-electronic decoder-based co-design provides a novel information hiding camera interface that is both high-speed and energy-efficient, offering an intriguing solution for visual information security.

翻译：诸如密码学之类的数据保护方法，尽管有效，却无意中暴露了秘密通信的存在，从而引起不必要的注意。本文介绍了一种与电子解码器集成的光学信息隐藏相机，并通过深度学习联合优化。该信息隐藏-解码系统采用衍射光学处理器作为前端，将输入图像变换并隐藏为看似普通的图案，以欺骗/误导人类观察者。这种信息隐藏变换适用于无限多种秘密消息的组合，所有这些组合都会被全光学地、通过光学处理器内被动的光-物质相互作用，转换为看似普通的输出图案。通过处理这些看似普通的输出图像，联合训练的电子解码器神经网络能够精确重建隐藏在欺骗性输出图案中的原始信息。我们通过设计一种信息隐藏衍射相机及联合优化的卷积解码器神经网络，在数值上验证了该方法。该系统在不同光照条件和噪声水平下的有效性得到了证明，显示出其鲁棒性。我们进一步将该信息隐藏相机扩展到多光谱操作，允许在不同波长下同时隐藏和解码多幅图像，所有这些都在单次前馈操作中完成。我们利用太赫兹辐射在实验上也证明了该框架的可行性。这种基于光学编码器-电子解码器的协同设计，提供了一种既高速又节能的新型信息隐藏相机接口，为视觉信息安全提供了一种引人入胜的解决方案。