Biblio

This paper illuminates the problem of non-secure DNS dynamic updates, which allow a miscreant to manipulate DNS entries in the zone files of authoritative name servers. We refer to this type of attack as to zone poisoning. This paper presents the first measurement study of the vulnerability. We analyze a random sample of 2.9 million domains and the Alexa top 1 million domains and find that at least 1,877 (0.065%) and 587 (0.062%) of domains are vulnerable, respectively. Among the vulnerable domains are governments, health care providers and banks, demonstrating that the threat impacts important services. Via this study and subsequent notifications to affected parties, we aim to improve the security of the DNS ecosystem.

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge (\textbackslashemph\e.g.\, semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \textbackslashemph\zero-shot hashing\ (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels (i.e., 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.