545 research outputs found
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 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
where is the number of faulty signatures. Our attacks are
heuristic but very efficient in practice. Furthermore, the new bound
can be extended up to 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
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