Forgery attack on optical encryption based on computational ghost imaging

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.

High-quality and high-diversity conditionally generative ghost imaging based on denoising diffusion probabilistic model

Deep-learning (DL) methods have gained significant attention in ghost imaging (GI) as promising approaches to attain high-quality reconstructions with limited sampling rates. However, existing DL-based GI methods primarily emphasize pixel-level loss and one-to-one mapping from bucket signals or low-quality GI images to high-quality images, tending to overlook the diversity in image reconstruction. Interpreting image reconstruction from the perspective of conditional probability, we propose the utilization of the denoising diffusion probabilistic model (DDPM) framework to address this challenge. Our designed method, known as DDPMGI, can not only achieve better quality but also generate reconstruction results with high diversity. At a sampling rate of 10%, our method achieves an average PSNR of 21.19 dB and an SSIM of 0.64, surpassing the performance of other comparison methods. The results of physical experiments further validate the effectiveness of our approach in real-world scenarios. Furthermore, we explore the potential application of our method in color GI reconstruction, where the average PSNR and SSIM reach 20.055 dB and 0.723, respectively. These results highlight the significant advancements and potential of our method in achieving high-quality image reconstructions in GI, including color image reconstruction.

Bipolar compressive ghost imaging method to improve imaging quality

Compressive ghost imaging (CGI) can effectively reduce the number of measurements required for ghost imaging reconstruction. In most cases, however, when using illumination patterns as measurement matrices, CGI has not demonstrated the ability to reconstruct high-quality images at an ultra-low sampling rate as perfect as claimed by compressive sensing theory. According to our analysis, the reason is that the non-negative nature of light intensity causes measurement matrix in compressive ghost imaging to be inconsistent with the essential requirements of good measurement matrix in compressive sensing theory, leading to low reconstruction quality. Aiming at this point, we propose a bipolar compressive ghost imaging method to improve the reconstruction quality of ghost imaging. The validity of the proposed method is proven by simulations and experiments.

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.

Experimental optical encryption based on random mask encoding and deep learning

We present an experimental scheme for optical encryption using random mask encoding and deep learning technique. A phase image is encrypted into a speckle pattern by a random amplitude modulation in the optical transmission. Before decryption processing, a neural network model is used to learn the mapping relationship between the pure-phase object and the speckle image rather than characterizing the filter film used in the scheme explicitly or parametrically. The random binary mask is made by a polyethylene terephthalate film and 2500 object-speckle pairs are used for training. The experimental results demonstrate that the proposed scheme based on deep learning could be successfully used as a random binary mask encrypted image processor, which can quickly output the primary image with high quality from the cyphertext.

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.

Deep neural network for coded mask cryptographical imaging

We proposed a novel cryptographic imaging scheme that is the combination of optical encryption and computational decryption. To prevent personal privacy from being spied upon amid the imaging formation process, in this study we applied a coded mask to optically encrypt the scene and utilized the deep neural network for computational decryption. For encryption, the sensor recorded a new representation of the original signal, not being distinguishable by humans on purpose. For decryption, we successfully reconstructed the image with the mean squared error equal to 0.028, and 100% for the classification through the Japanese Female Facial Expression dataset. By means of the feature visualization, we found that the coded mask served as a linear operator to synthesize the spatial fidelity of the original scene, but kept the features for the post-recognition process. We believe the proposed framework can inspire more possibilities for the unconventional imaging system.

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.