Optical cryptosystem based on computational ghost imaging and nonlinear authentication.

We propose an optical encryption system that combines computational ghost imaging (CGI) with image authentication to enhance security. In this scheme, Hadamard patterns are projected onto the secret images, while their reflected light intensities are captured using a bucket detector (BD). To further strengthen the security of the collected secret data, we encrypt it as a series of binary matrices serving as ciphertext. During the authentication key generation, these encoded binary matrices serve as illumination patterns in the CGI system for a non-secret image, which is used as a reference image for authentication. The data captured by the BD is then binarized to generate the authentication key. Upon successful authentication, the receiver obtains the decryption keys. This method achieves both data compression for secret images and enhanced security during information transmission. We validate the feasibility of this method through computer simulations and optical experiments.

Speckle-based optical encryption with complex-amplitude coding and deep learning.

We propose a speckle-based optical encryption scheme by using complex-amplitude coding and deep learning, which enables the encryption and decryption of complex-amplitude plaintext containing both amplitude and phase images. During encryption, the amplitude and phase images are modulated using a superpixel-based coding technique and feded into a digital micromirror device. After passing through a 4f system, the information undergoes disturbance modulation by a scattering medium, resulting in a diffracted speckle pattern serving as the ciphertext. A Y-shaped convolutional network (Y-Net) model is constructed to establish the mapping relationship between the complex-amplitude plaintext and ciphertext through training. During decryption, the Y-Net model is utilized to quickly extract high-quality amplitude and phase images from the ciphertext. Experimental results verify the feasibility and effectiveness of our proposed method, demonstrating that the potential of integrating speckle encryption and deep learning for optical complex-amplitude encryption.

Information security scheme using deep learning-assisted single-pixel imaging and orthogonal coding.

Providing secure and efficient transmission for multiple optical images has been an important issue in the field of information security. Here we present a hybrid image compression, encryption and reconstruction scheme based on deep learning-assisted single-pixel imaging (SPI) and orthogonal coding. In the optical SPI-based encryption, two-dimensional images are encrypted into one-dimensional bucket signals, which will be further compressed by a binarization operation. By overlaying orthogonal coding on the compressed signals, we obtain the ciphertext that allows multiple users to access with the same privileges. The ciphertext can be decoded back to the binarized bucket signals with the help of orthogonal keys. To enhance reconstruction efficiency and quality, a deep learning framework based on DenseNet is employed to retrieve the original optical images. Numerical and experimental results have been presented to verify the feasibility and effectiveness of the proposed scheme.

Steganographic optical image encryption based on single-pixel imaging and an untrained neural network.

We propose a steganographic optical image encryption based on single-pixel imaging (SPI) and an untrained neural network. In this encryption scheme, random binary illumination patterns are projected onto a secret image and light intensities reflected from the image are then detected by a bucket detector (BD). To enhance the security of collected secret data, a steganographic approach is introduced in this method, which implements data hiding with a SPI system using encoded illumination patterns. A non-secret image is illuminated with a sequence of encoded patterns that were generated from the scrambled measurements of secret image, and sequential cyphertext data can be obtained by collecting the diffraction data with the BD. Different from traditional SPI-based encryption schemes, an untrained neural network is adopted as a SPI-encrypted image processor, which allows to reduce time spent on data preparation and reconstruct the secret images with high quality. Both computer simulations and optical experiments are carried out to demonstrate the feasibility of the method.