{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T03:16:14Z","timestamp":1785381374939,"version":"3.55.0"},"reference-count":28,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T00:00:00Z","timestamp":1596412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2020,8,3]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Most source recording device identification models for Web media forensics are based on a single feature to complete the identification task and often have the disadvantages of long time and poor accuracy. The purpose of this paper is to propose a new method for end-to-end network source identification of multi-feature fusion devices.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This paper proposes an efficient multi-feature fusion source recording device identification method based on end-to-end and attention mechanism, so as to achieve efficient and convenient identification of recording devices of Web media forensics.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The authors conducted sufficient experiments to prove the effectiveness of the models that they have proposed. The experiments show that the end-to-end system is improved by 7.1% compared to the baseline i-vector system, compared to the authors\u2019 previous system, the accuracy is improved by 0.4%, and the training time is reduced by 50%.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>With the development of Web media forensics and internet technology, the use of Web media as evidence is increasing. Among them, it is particularly important to study the authenticity and accuracy of Web media audio.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper aims to promote the development of source recording device identification and provide effective technology for Web media forensics and judicial record evidence that need to apply device source identification technology.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-06-2020-0038","type":"journal-article","created":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T09:39:24Z","timestamp":1596706764000},"page":"413-425","source":"Crossref","is-referenced-by-count":23,"title":["An end-to-end deep source recording device identification system for Web media forensics"],"prefix":"10.1108","volume":"16","author":[{"given":"Chunyan","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongliang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"key":"key2020100708375230100_ref001","first-page":"116","article-title":"Regularized nonlinear discriminant analysis \u2013 an approach to robust dimensionality reduction for data visualization","volume-title":"International Joint Conference on Computer Vision","year":"2017"},{"issue":"5","key":"key2020100708375230100_ref002","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/LSP.2006.870086","article-title":"Support vector machines using GMM supervectors for speaker verification","volume":"13","year":"2006","journal-title":"IEEE Signal Processing Letters"},{"issue":"4","key":"key2020100708375230100_ref003","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/TASL.2010.2064307","article-title":"Front-end factor analysis for speaker verification","volume":"19","year":"2011","journal-title":"IEEE Transactions on Audio, Speech, and Language Processing"},{"key":"key2020100708375230100_ref004","first-page":"1410","article-title":"Neural turing machines","year":"2014","journal-title":"Computer Science"},{"key":"key2020100708375230100_ref005","doi-asserted-by":"crossref","unstructured":"Hanil\u00e7i, C. and Kinnunen, T. 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