Code reuse not only brings convenience to software development， but also introduces security risks， such as accelerating vulnerability propagation and malicious code plagiarism. Code similarity detection technology is to calculate code similarity by analyzing lexical， syntactic， semantic and other information between codes. It is one of the most effective technologies to judge code reuse， and it is also a program security analysis technology that has developed rapidly in recent years. First， the latest technical progress of code similarity detection was systematically reviewed， and the current code similarity detection technology was classified. According to whether the target code was open source， it was divided into source code similarity detection and binary code similarity detection. According to the different programming languages and instruction sets， the second subdivision was carried out. Then， the ideas and research results of each technology were summarized， the successful cases of machine learning technology in the field of code similarity detection were analyzed， and the advantages and disadvantages of existing technologies were discussed. Finally， the development trend of code similarity detection technology was given to provide reference for relevant researchers.

To effectively improve the denoising effect of the original anisotropic diffusion model that used only the 4 neighborhood pixels information and ignored the diagonal neighborhood pixels information of the pixel to be repaired in the image denoising process, a image denoising algorithm using UK-flag shaped anisotropic diffusion model was proposed. This model not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also used another 4 diagonal neighborhood pixels information in the denoising process. Then the model using the 8 direction pixels information for image denoising was presented, and it was proved to be rational. The proposed algorithm, the original algorithm, and an improved similar algorithm were used to remove the noise from 4 images with noise. The experimental results show that the proposed algorithm has an average increase of 1.90dB and 1.43dB in Peak Signal-to-Noise Ratio (PSNR) value respectively, and an average increase of 0.175 and 0.1 in Mean Structure Similitary Index (MSSIM) value respectively, compared with the original algorithm and the improved similar algorithm, which concludes that the proposed algorithm is more suitable for image denoising. algorithm not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also another 4 diagonal neighborhood pixels information was used in the denoising process, and the algorithm was proved to be rationality. The experimental results showed that the proposed algorithm could increase the PSNR (peak signal-to-noise ratio) value 1.69db, and the MSSIM(mean structure similitary index) value 0.14, compared with the other similar algorithms in image denoising, which conclud that this proposed algorithm is more suitable for image denoising.