On the Multiple Fault Attack on RSA Signatures with LSBs of Messages Unknown

In CHES 2009, Coron, Joux, Kizhvatov, Naccache and Paillier(CJKNP) introduced a fault attack on RSA signatures with partially unknown messages. They factored RSA modulus $N$ using a single faulty signature and increased the bound of unknown messages by multiple fault attack, however, the complexity multiple fault attack is exponential in the number of faulty signatures. At RSA 2010, it was improved which run in polynomial time in number of faults. Both previous multiple fault attacks deal with the general case that the unknown part of message is in the middle. This paper handles a special situation that some least significant bits of messages are unknown. First, we describe a sample attack by utilizing the technique of solving simultaneous diophantine approximation problem, and the bound of unknown message is $N^{\frac1{2}-\frac1{2\ell}}$ where $\ell$ is the number of faulty signatures. Our attacks are heuristic but very efficient in practice. Furthermore, the new bound can be extended up to $N^{\frac1{2}^{1+\frac1{\ell}}}$ by the Cohn-Heninger technique. Comparison between previous attacks and new attacks with LSBs of message unknown will be given by simulation test

中国近代劇成立期における日本新劇運動の受容 : 春柳社を中心に

取得学位:博士（文学）, 授与番号:博士甲第89号, 授与年月日:2007年3月22日, 授与大学:金沢大学, 論文審査委員長:上田, 正行, 論文審査委員:大瀧, 幸子 / 上田, 望 / 西村, 聡 / 木越,

Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model

This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing auxiliary images, there are still some deficiencies, including the poor generalization ability of the model and insufficient fine-grained semantic information for extracted features. In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively. Specifically, to overcome the lack of prior information caused by the small-scale dataset, we employ a pre-trained vision-language model that is trained with a large amount of image-text pairs. Therefore, the textual and visual encoders of this large-scale model have stronger generalization ability. Then, we propose a multi-stage feature fusion strategy to fuse the textual and visual features into the backbone network progressively. Meanwhile, to further explore the effectiveness of fine-grained text descriptions for point cloud completion, we also build a text corpus with fine-grained descriptions, which can provide richer geometric details for 3D shapes. The rich text descriptions can be used for training and evaluating our network. Extensive quantitative and qualitative experiments demonstrate the superior performance of our method compared to state-of-the-art point cloud completion networks