  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.
