Computational ghost imaging with spatiotemporal encoding pseudo-random binary patterns

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.

An optical multiple-image authentication based on computational ghost imaging and total-variation minimization

An optical multiple-image authentication is suggested using computational ghost imaging and total-variation minimization. Differing from encrypting multiple images into a noise-like ciphertext directly, as described in most conventional authentication methods, the related encoded information is embedded into a cover image to avoid the attention of eavesdroppers. First, multiple images are encoded to form real-valued sequences composed of corresponding bucket values obtained by the aid of computational ghost imaging, and four sub-images are obtained by decomposing the cover image using wavelet transform. Second, measured sequences are embedded into one of the sub-images, and embedding positions are randomly selected using corresponding binary masks. To enhance the security level, a chaotic sequence is produced using logistic map and used to scramble measured intensities. Most importantly, original images with high quality can be directly recovered using total-variation minimization. The validity and robustness of the proposed approach are verified with optical experiments.

Optical multiple-image authentication based on computational ghost imaging and hybrid non-convex second-order total variation

An optical security method for multiple-image authentication is proposed based on computational ghost imaging and hybrid non-convex second-order total variation. Firstly, each original image to be authenticated is encoded to the sparse information using computational ghost imaging, where illumination patterns are generated based on Hadamard matrix. In the same time, the cover image is divided into four sub-images with wavelet transform. Secondly, one of sub-images with low-frequency coefficients is decomposed using singular value decomposition (SVD), and all sparse data are embedded into the diagonal matrix with the help of binary masks. To enhance the security, the generalized Arnold transform is used to scramble the modified diagonal matrix. After using SVD again, the marked cover image carrying the information of multiple original images is obtained using the inverse wavelet transform. In the authentication process, the quality of each reconstructed image can be greatly improved based on hybrid non-convex second-order total variation. Even at a very low sampling ratio (i.e., 6%), the existence of original images can be efficiently verified using the nonlinear correlation maps. To our knowledge, it is first to embed sparse data into the high-frequency sub-image using two cascaded SVDs, which can guarantee high robustness against the Gaussian filter and sharpen filter. The optical experiments demonstrate the feasibility of the proposed mechanism, which can provide an effective alternative for the multiple-image authentication.

Lossy and noisy channel simulation in computational ghost imaging by using noise-induced pattern

We provide a method to evaluate effects of a lossy and noisy optical channel in computational ghost imaging (CGI) technique. Instead of preparing an external noise source, we simulate the optical channel with a basic CGI experiment using programmatically generated noise-induced patterns. By using our method, we show that CGI can reject a noise of which intensity is similar with an imaging signal intensity at a target. The results with our method are well matched with experimental ones including external noise source. This method would provide useful knowledge to analyze environmental effects in CGI without realization of the environment.

Gigahertz-rate random speckle projection for high-speed single-pixel image classification

Imaging techniques based on single-pixel detection, such as ghost imaging, can reconstruct or recognize a target scene from multiple measurements using a sequence of random mask patterns. However, the processing speed is limited by the low rate of the pattern generation. In this study, we propose an ultrafast method for random speckle pattern generation, which has the potential to overcome the limited processing speed. The proposed approach is based on multimode fiber speckles induced by fast optical phase modulation. We experimentally demonstrate dynamic speckle projection with phase modulation at 10 GHz rates, which is five to six orders of magnitude higher than conventional modulation approaches using spatial light modulators. Moreover, we combine the proposed generation approach with a wavelength-division multiplexing technique and apply it for image classification. As a proof-of-concept demonstration, we show that 28×28-pixel images of digits acquired at GHz rates can be accurately classified using a simple neural network. The proposed approach opens a novel pathway for an all-optical image processor.

Multi-Party Cryptographic Key Distribution Protocol over a Public Network Based on a Quick-Response Code

In existing cryptographic key distribution (CKD) protocols based on computational ghost imaging (CGI), the interaction among multiple legitimate users is generally neglected, and the channel noise has a serious impact on the performance. To overcome these shortcomings, we propose a multi-party interactive CKD protocol over a public network, which takes advantage of the cascade ablation of fragment patterns (FPs). The server splits a quick-response (QR) code image into multiple FPs and embeds different "watermark" labels into these FPs. By using a CGI setup, the server will acquire a series of bucket value sequences with respect to different FPs and send them to multiple legitimate users through a public network. The users reconstruct the FPs and determine whether there is an attack in the public channel according to the content of the recovered "watermark" labels, so as to complete the self-authentication. Finally, these users can extract their cryptographic keys by scanning the QR code (the cascade ablation result of FPs) returned by an intermediary. Both simulation and experimental results have verified the feasibility of this protocol. The impacts of different attacks and the noise robustness have also been investigated.

Holographic and speckle encryption using deep learning

Vulnerability analysis of optical encryption schemes using deep learning (DL) has recently become of interest to many researchers. However, very few works have paid attention to the design of optical encryption systems using DL. Here we report on the combination of the holographic method and DL technique for optical encryption, wherein a secret image is encrypted into a synthetic phase computer-generated hologram (CGH) by using a hybrid non-iterative procedure. In order to increase the level of security, the use of the steganographic technique is considered in our proposed method. A cover image can be directly diffracted by the synthetic CGH and be observed visually. The speckle pattern diffracted by the CGH, which is decrypted from the synthetic CGH, is the only input to a pre-trained network model. We experimentally build and test the encryption system. A dense convolutional neural network (DenseNet) was trained to estimate the relationship between the secret images and noise-like diffraction patterns that were recorded optically. The results demonstrate that the network can quickly output the primary secret images with high visual quality as expected, which is impossible to achieve with traditional decryption algorithms.

Fractional Fourier single-pixel imaging

Single-pixel imaging technology has a number of advantages over conventional imaging approaches, such as wide operation wavelength region, compressive sampling, low light radiation dose and insensitivity to distortion. Here, we report on a novel single-pixel imaging based on fractional Fourier transform (FRFT), which captures images by acquiring the fractional-domain information of targets. With the use of structured illumination of two-dimensional FRFT base patterns, FRFT coefficients of the object could be measured by single-pixel detection. Then, the object image is achieved by performing inverse FRFT on the measurements. Furthermore, the proposed method can reconstruct the object image from sub-Nyquist measurements because of the sparsity of image data in fractional domain. In comparison with traditional single-pixel imaging, it provides a new degree of freedom, namely fractional order, and therefore has more flexibility and new features for practical applications. In experiments, the proposed method has been applied for edge detection of object, with an adjustable parameter as a new degree of freedom.

Preventing forgery attacks in computational ghost imaging or disabling ghost imaging in a "spatiotemporal" scattering medium with weighted multiplicative signals

The ghost imaging (GI) approach is an intriguing and promising image acquisition technique that can transmit high-quality image information in a scattering environment. In this paper, we focus on two concerns recently emerged in the GI modality: one is the vulnerability to forgery attacks in GI-based optical encryption [Opt. Lett.45, 3917 (2020)OPLEDP0146-959210.1364/OL.392424], and the other is the potential threat of GI to personal privacy regarding non-invasive imaging [Opt. Express28, 17232 (2020)OPEXFF1094-408710.1364/OE.391788]. The core idea is to recommend introducing weighted multiplicative signals [Opt. Express27, 36505 (2019)OPEXFF1094-408710.1364/OE.27.036505] into the computational GI system, whether on the transmitting end or the receiving end. At the transmitting end, the random multiplicative signal can be used as an additional key that can reduce the possibility of forgery attacks, thereby increasing image transmission security. On the receiving end, the introduction of a random multiplicative signal to a spatial scattering medium makes it a "spatiotemporal" scattering medium, whose transmittance changes with time. Further, the spatiotemporal scattering medium can disable direct imaging and GI at the same time with low cost, thereby having great potential in privacy protection in daily lives.