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As a promising technique, the spatial information of an object can be acquired by employing active illumination of sinusoidal patterns in the Fourier single-pixel imaging. However, the major challenge in this field is that a large number of illumination patterns should be generated to record measurements in order to avoid the loss of object details. In this paper, an optical multiple-image authentication method is proposed based on sparse sampling and multiple logistic maps. To improve the measurement efficiency, object images to be authenticated are randomly sampled based on the spatial frequency distribution with smaller size, and the Fourier sinusoid patterns generated for each frequency are converted into binarized illumination patterns using the Floyd-Steinberg error diffusion dithering algorithm. In the generation process of the ciphertext, two chaotic sequences are used to randomly select spatial frequency for each object image and scramble all measurements, respectively. Considering initial values and bifurcation parameters of logistic maps as secret keys, the security of the cryptosystem can be greatly enhanced. For the first time to our knowledge, how to authenticate the reconstructed object image is implemented using a significantly low number of measurements (i.e., at a very low sampling ratio less than 5% of Nyquist limit) in the Fourier single-pixel imaging. The experimental results as well as simulations illustrate the feasibility of the proposed multiple-image authentication mechanism, which can provide an effective alternative for the related research.

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.