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.
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.
Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption.
Abstract Optical encryption is a potential scheme for information security that exploits abundant degrees of freedom of light to encode information. However, conventional encryption based on fluorescent materials faces challenges in handling complex secret information. Alternatively, single‐pixel imaging (SPI) provides a computational modality to solve these problems. In this study, a high‐capacity fluorescence encryption scheme, achieved by introducing lanthanide materials and steganography into the encoding and decoding processes of SPI is proposed. Two types of well‐designed lanthanide luminescent materials are utilized and excited to generate fluorescence images (fluo‐images), which are crucial in this scheme. Various practical experiments using fluo‐images as secret keys demonstrate the robustness, effectiveness, and repeatability of this scheme. Furthermore, multi‐image experiments indicate the potential of this method to increase secret information capacity. Thus, the proposed fluorescence encryption scheme does provide an efficient computational encryption strategy based on lanthanide luminescent materials for information security, which can improve the security of traditional optical encryption and simultaneously enhance the flexibility of SPI computational decryption.
Single-pixel imaging (SPI) can transform 2D or 3D image data into 1D light signals, which offers promising prospects for image compression and transmission. However, during data communication these light signals in public channels will easily draw the attention of eavesdroppers. Here, we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission. This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection. Then the transformation parameters serve as the secret key, while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism. Compared with existing encryption schemes, both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules. Additionally, this approach solves the problem of secure key transmission, thus ensuring the security of information and the quality of the decrypted images.
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.
In the rapidly evolving field of optical information security, single-pixel imaging (SPI) has emerged as a promising technique for hidden information transmission. However, traditional SPI methods face significant challenges, including the need for excessive modulation patterns and the vulnerability of encrypted information during transmission. Furthermore, the field lacks efficient methods to reconstruct both plaintext and ciphertext images from the same set of single-pixel measurements. Here, we propose a novel and efficient encryption strategy for Fourier single-pixel imaging (FSPI) that addresses these critical challenges. Our approach integrates two key innovations: a two-step Fourier-total variation conjugate gradient descent (F-TVCGD) method and a dual-key decryption mechanism. The F-TVCGD method significantly reduces the number of modulation patterns required for image reconstruction, enhancing efficiency and minimizing data redundancy. Our dual-key mechanism enables the reconstruction of both plaintext and ciphertext images from a single set of single-pixel measurements using different decryption keys, significantly enhancing security without compromising efficiency. The incorporation of Fourier symmetric patterns improves the convergence robustness of the symmetric gradient descent (SGD) algorithm, leading to superior performance under challenging conditions such as sparse sampling and noise attacks. Numerical simulations and optical experiments validate our method's improvements in both accuracy and security compared to traditional approaches. Our findings demonstrate that the proposed F-TVCGD and SGD strategies effectively address the challenges of excessive modulation patterns and information vulnerability in FSPI.
An optical image encryption method is proposed based on Fourier single-pixel imaging and iterated phase retrieval algorithm. First, a binary barcode image containing two groups of horizontal strips is randomly generated and used as the target image in the single-pixel imaging process. Second, considering the barcode image as the amplitude constraint, the original plaintext image is encoded to two phase-only masks using a designed phase retrieval algorithm in Fresnel domain, and these masks are applied as secret keys. Finally, the barcode image is encrypted to a series of measurements using two-step phase shift Fourier single-pixel imaging as the ciphertext. Differing other methods, the original image is not directly imaged and encoded into the ciphertext. Even the ciphertext is obtained by a cracker, only the barcode image may be further discovered. Due to two phase-only masks as secret keys, the security level of the cryptosystem can be enhanced greatly. The simulated results verify the feasibility of the proposed method, and this work provides an effective alternative for the related research.