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Articles by Xiamu Niu
Total Records ( 3 ) for Xiamu Niu
  Abid Khan , Xiamu Niu and Zhang Yong
  A data integrity solution for mobile agents is presented. The proposed scheme combines digital watermarking and digital signature to achieve the desired security requirements of strong forward integrity, truncation resilience and non-repudiation. The results computed at each hop are first watermarked and then digitally signed. When the agent returns to the home platform we verify the signature first and then extract watermark. Any tampering with the results can be determined in the verification process. We have implemented the proposed scheme with various hashing algorithms like SHA-1, SHA-256 and SHA-512 for a price comparison scenario. For digital signature we have used RSA based signature. We have applied t-test to check the significance of present results. Present experimental results suggest that it can be used in an e-commerce application.
  Hui Zhang , Qiong Li , Haibin Zhang and Xiamu Niu
  This study proposed a novel benchmark for evaluating the robustness and discriminability properties of perceptual hashing algorithms. Firstly, two major problems neglected by traditional benchmark are analyzed thoroughly with a concrete experiment. One problem is the inconsistence between the subjective feeling and the objective perceptual distance, the other is the partiality of the performance for different attacks. And then, in order to overcome the problems, a new benchmark for perceptual hashing based on human subjective identification is proposed and the corresponding evaluation methods are presented by illustrative experiments and examples. Present benchmark methods are fairer and more comprehensive than the traditional methods.
  Tiejun Zhang , Qi Han , Ahmed A. Abd El-Latif , Xuefeng Bai and Xiamu Niu
  Cartoon pirate uploading is a very serious problem for the image and video-sharing website. In this study, we propose a new method to detect the characters in 2D-cartoon images, aiming at rejecting pirate uploading automatically. We extract the curve in the cartoon image as the main content and then design a local shape feature named Scalable-Shape Context (SSC) to present the local shape of cartoon. Firstly, we use the Harris-Laplace corner detector to find the key points at multi-scale in the cartoon image, most of which are localized at the junctions of curves. Secondly, the scale of each key point is used as a reference scale for Shape Context (SC) to describe the curvilinear structure around the key points. Then, the matching problem between the key points extracted from the input model and testing image is solved as an optimal assignment problem. Finally, a Hough-voting scheme is employed to find the location of the character in the testing image. The experimental results show that the proposed SSC-based detection method is effective in the detection of 2D-cartoon characters.
 
 
 
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