52,020 research outputs found

    Electronic marking and identification techniques to discourage document copying

    Get PDF
    Modern computer networks make it possible to distribute documents quickly and economically by electronic means rather than by conventional paper means. However, the widespread adoption of electronic distribution of copyrighted material is currently impeded by the ease of illicit copying and dissemination. In this paper we propose techniques that discourage illicit distribution by embedding each document with a unique codeword. Our encoding techniques are indiscernible by readers, yet enable us to identify the sanctioned recipient of a document by examination of a recovered document. We propose three coding methods, describe one in detail, and present experimental results showing that our identification techniques are highly reliable, even after documents have been photocopied

    Fully dynamic data structure for LCE queries in compressed space

    Get PDF
    A Longest Common Extension (LCE) query on a text TT of length NN asks for the length of the longest common prefix of suffixes starting at given two positions. We show that the signature encoding G\mathcal{G} of size w=O(min(zlogNlogM,N))w = O(\min(z \log N \log^* M, N)) [Mehlhorn et al., Algorithmica 17(2):183-198, 1997] of TT, which can be seen as a compressed representation of TT, has a capability to support LCE queries in O(logN+loglogM)O(\log N + \log \ell \log^* M) time, where \ell is the answer to the query, zz is the size of the Lempel-Ziv77 (LZ77) factorization of TT, and M4NM \geq 4N is an integer that can be handled in constant time under word RAM model. In compressed space, this is the fastest deterministic LCE data structure in many cases. Moreover, G\mathcal{G} can be enhanced to support efficient update operations: After processing G\mathcal{G} in O(wfA)O(w f_{\mathcal{A}}) time, we can insert/delete any (sub)string of length yy into/from an arbitrary position of TT in O((y+logNlogM)fA)O((y+ \log N\log^* M) f_{\mathcal{A}}) time, where fA=O(min{loglogMloglogwlogloglogM,logwloglogw})f_{\mathcal{A}} = O(\min \{ \frac{\log\log M \log\log w}{\log\log\log M}, \sqrt{\frac{\log w}{\log\log w}} \}). This yields the first fully dynamic LCE data structure. We also present efficient construction algorithms from various types of inputs: We can construct G\mathcal{G} in O(NfA)O(N f_{\mathcal{A}}) time from uncompressed string TT; in O(nloglognlogNlogM)O(n \log\log n \log N \log^* M) time from grammar-compressed string TT represented by a straight-line program of size nn; and in O(zfAlogNlogM)O(z f_{\mathcal{A}} \log N \log^* M) time from LZ77-compressed string TT with zz factors. On top of the above contributions, we show several applications of our data structures which improve previous best known results on grammar-compressed string processing.Comment: arXiv admin note: text overlap with arXiv:1504.0695

    Synthetic speech detection and audio steganography in VoIP scenarios

    Get PDF
    The distinction between synthetic and human voice uses the techniques of the current biometric voice recognition systems, which prevent that a person’s voice, no matter if with good or bad intentions, can be confused with someone else’s. Steganography gives the possibility to hide in a file without a particular value (usually audio, video or image files) a hidden message in such a way as to not rise suspicion to any external observer. This article suggests two methods, applicable in a VoIP hypothetical scenario, which allow us to distinguish a synthetic speech from a human voice, and to insert within the Comfort Noise a text message generated in the pauses of a voice conversation. The first method takes up the studies already carried out for the Modulation Features related to the temporal analysis of the speech signals, while the second one proposes a technique that derives from the Direct Sequence Spread Spectrum, which consists in distributing the signal energy to hide on a wider band transmission. Due to space limits, this paper is only an extended abstract. The full version will contain further details on our research

    siEDM: an efficient string index and search algorithm for edit distance with moves

    Full text link
    Although several self-indexes for highly repetitive text collections exist, developing an index and search algorithm with editing operations remains a challenge. Edit distance with moves (EDM) is a string-to-string distance measure that includes substring moves in addition to ordinal editing operations to turn one string into another. Although the problem of computing EDM is intractable, it has a wide range of potential applications, especially in approximate string retrieval. Despite the importance of computing EDM, there has been no efficient method for indexing and searching large text collections based on the EDM measure. We propose the first algorithm, named string index for edit distance with moves (siEDM), for indexing and searching strings with EDM. The siEDM algorithm builds an index structure by leveraging the idea behind the edit sensitive parsing (ESP), an efficient algorithm enabling approximately computing EDM with guarantees of upper and lower bounds for the exact EDM. siEDM efficiently prunes the space for searching query strings by the proposed method, which enables fast query searches with the same guarantee as ESP. We experimentally tested the ability of siEDM to index and search strings on benchmark datasets, and we showed siEDM's efficiency.Comment: 23 page

    IVOA Recommendation: VOTable Format Definition Version 1.3

    Full text link
    This document describes the structures making up the VOTable standard. The main part of this document describes the adopted part of the VOTable standard; it is followed by appendices presenting extensions which have been proposed and/or discussed, but which are not part of the standard
    corecore