In order to reduce the matching error rate of ORB (Oriented FAST and Rotated BRIEF) method caused by the scale invariance of the feature points in the algorithm and enhance the robustness of the descriptors of Binary Robust Independent Elementary Features (BRIEF) algorithm to noise, an improved feature point matching method was proposed. Speeded-Up Robust Features (SURF) algorithm was used to extract feature points, and BRIEF algorithm with direction information was used to describe the feature points. Random pixel pairs in the neighborhood of the feature point were selected, the comparison results of the grayscales and the similarity of pixel pairs were encoded respectively, and Hamming distance was used to calculate the differences between the two codes. The similarity between the feature points were measured by the adaptive weighted fusion method. Experimental results show that the improved method has better adaptability to the scale variance, illumination variance and blurred variance of images, can obtain a higher feature point correct matching rate compared with the conventional ORB method, and can be used to improve the performance of image stitching.

To solve the problems of small fault depth and complex manual deduction in previous fault attacks on SIMON, an Algebraic Fault Attack (AFA) method was proposed. Firstly, Correct equations of full-round SIMON encryption was established based on the algebraic representation of SIMON core operation ‘&’. Then faults were injected into the internal states and two models were provided for fault representation based on whether attackers knew the exact fault information or not. Finally, a CryptoMinisat-2.9.6 solver was used for round-keys recovery. The simulation results show that the fault-known and fault-unknown model need 5 and 6 faults to recover the entire key set with single-bit faults injected in the 26th round of SIMON32/64. As for SIMON128/128, two models both need only 2 faults to recover the entire key set with n-bit length faults injected in the 65th round. Moreover, it can be found that the influencing factor of average solving time will change from fault information to computation with fault number growing.

Concerning the poor efficiency in unknown protocol frame segmentation, an unknown protocol frame segmentation algorithm based on preamble mining was proposed. Firstly, the principle of the preamble being used as the start of frame was introduced. As the cause that the existing frequent sequence mining algorithm cannot mine long preamble directly, the problems in candidate sequence selection were analyzed. Combining with the characteristics of preamble, two methods for selecting candidate sequences from the target bit streams and selecting candidate sequence based on the variation of the size of candidate sequence set were given. Secondly, an algorithm inferring the length of preamble and mining the preamble was put forward for unknown protocol frame segmentation. Finally, experiments were conducted with real bit streams captured from the Ethernet. The experimental results show that the proposed algorithm can rapidly and accurately mine the preamble sequence in the bit stream of the unknown protocol with lower space and time complexity.

Focusing on the issue that most of the existing evaluation methods transform images into different coordinate domain, a spatial Natural Scene Statistics (NSS) based model of no reference stereoscopic image quality assessment method was proposed. Among the stereoscopic image quality assessment, in order to better combine with the binocular visual features of human beings, left and right images were fused to construct a cyclopean map. Firstly, via statistical distribution of the Cyclopean Mean Subtracted Contrast Normalized (CMSCN) coefficients, the natural scene statistical characteristics were extracted in spatial domain from the cyclopean map. Secondly, by getting statistical distribution of the Disparity Mean Subtracted Contrast Normalized (DMSCN) coefficients, and the same characteristics were extracted from the disparity map obtained by optical flow model. Finally, Support Vector Regression (SVR) was performed to predict the objective scores of stereoscopic images by establishing the relationship between the stereoscopic image feature information and the Difference Mean Opinion Score (DMOS). The experimental results show that compared with other methods, the Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SROCC) indicators reach 0.94 on symmetric stereoscopic image database, and the PLCC indicator reaches 0.91 and the SROCC indicator reaches 0.93 on asymmetric stereoscopic image database, which indicate the proposed method can achieve higher consistency with subjective assessment of stereoscopic images.