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Current typical adaptive steganography algorithms take the detection resistant capability into account adequately but usually cannot extract the embedded secret messages correctly when stego images suffer from compression attack. In order to solve this problem, a framework of adaptive steganography resisting JPEG compression and detection is proposed. Utilizing the relationship between Discrete Cosine Transformation (DCT) coefficients, the domain of messages embedding is determined; for the maximum of the JPEG compression resistant ability, the modifying magnitude of different DCT coefficients caused by messages embedding can be determined; in order to ensure the completely correct extraction of embedded messages after JPEG compression, error correct codes are used to encode the messages to be embedded; on the basis of the current distortion functions, the distortion value of DCT coefficients corresponding to the modifying magnitude in the embedding domain can be calculated; to improve the detection resistant ability of the stego images and realize the minimum distortion embedding, syndrome‐trellis codes are used to embed the encoded messages into the DCT coefficients that have a smaller distortion value. Based on the proposed framework, an adaptive steganography algorithm resisting JPEG compression and detection is designed, which utilizes the relationship between coefficients in a DCT block and the means of that in three adjacent DCT blocks. The experimental results that demonstrate the proposed algorithm not only has a good JPEG compression resistant ability but also has a strong detection resistant performance. Comparing with current J‐UNIWARD steganography under quality factor 85 of JPEG compression, the extraction error rates without pre‐compression decrease from about 50% to nearly 0, while the stego images remain a good detection resistant ability comparing with a typical robust watermarking algorithm, which shows the validity of the proposed framework. Copyright © 2016 John Wiley & Sons, Ltd.

With the development of synchrotron radiation technology and the improvement of light source coherence, Ptychography has been developed rapidly. Ptychography algorithm solves the problems of slow convergence, easy to fall into the local optimal solution and stagnation of the traditional coherent diffraction imaging algorithm. It has the advantages of large imaging field of view, robustness of algorithm, high tolerance to error and wide range of applications, and is becoming a hot research direction in the field of coherent diffraction imaging. Ptychography reconstructs the complex amplitude distribution and illumination light of the sample by iterative algorithms, which can theoretically reach the resolution of the diffraction limit. It has excellent applications in the fields of wavefront detection, phase imaging and optical metrology. This paper first introduces the background of the proposed Ptychography algorithm and briefly describes the problem of coherent diffraction imaging algorithm and its development, and then summarizes the development of Ptychography algorithm in detail, mainly including the mainstream algorithm of Ptychography and its kernel. This paper then describes in detail the improvement algorithms corresponding to the improvement of the efficiency of Ptychography experiments, correction of position errors and the effect of illumination light multi-modal and elaborates the algorithm flow. After analyzing the possible intersection of diffraction imaging and neural networks in the field of artificial intelligence, this paper introduces new algorithms for combining Ptychography with artificial intelligence. New algorithms for Ptychography combined with neural networks will have potential for new applications in terms of generality, accuracy and robustness. Finally, a specific parallelization implementation of the Ptychography algorithm and common software packages are presented. The logic for writing the parallelization of the algorithm implementation of each package and the corresponding advantages and disadvantages of the packages are described in detail. The characteristics and performance of each package are then listed for reference. This paper helps to establish a global perspective on the algorithm itself, artificial intelligence and computational methods in the field of Ptychography, and is an important reference for the systematic development of Ptychography methodology.

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